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ACL-OCL / Base_JSON /prefixN /json /newsum /2021.newsum-1.12.json
Benjamin Aw
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 {
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T14:47:23.217394Z"
},
"title": "Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets",
"authors": [
{
"first": "Don",
"middle": [],
"last": "Tuggener",
"suffix": "",
"affiliation": {
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"institution": "Zurich University of Applied Sciences (ZHAW)",
"location": {
"settlement": "Winterthur",
"country": "Switzerland"
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},
"email": ""
},
{
"first": "Margot",
"middle": [],
"last": "Mieskes",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Applied Sciences",
"location": {
"settlement": "Darmstadt",
"country": "Germany"
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},
"email": "[email protected]"
},
{
"first": "Jan",
"middle": [],
"last": "Deriu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Zurich University of Applied Sciences (ZHAW)",
"location": {
"settlement": "Winterthur",
"country": "Switzerland"
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},
"email": "[email protected]"
},
{
"first": "Mark",
"middle": [],
"last": "Cieliebak",
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"affiliation": {
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"institution": "Zurich University of Applied Sciences (ZHAW)",
"location": {
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"abstract": "Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.",
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"text": "Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.",
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"text": "Dialogue summarization is a long-standing task in the NLP field and has recently gained traction through the emergence of novel data sets (Gliwa et al., 2019; Zhu et al., 2021) and community efforts like the AutoMin 1 shared task or the Summ-Dial@SIGDial 2021 special session 2 . Dialogues can take a wide variety of forms ranging from formal interviews on a specific topic, political debates to informal conversations over the telephone. 3 Therefore, the question of what is a suitable, appropriate summary of this type of data emerges. While abstractive and extractive summaries have emerged as the de-facto standard for summarization of continuous text (either single or multiple documents), the situation is less clear for dialogue summarization: What is a \"proper\" summary for the different types of dialogues?",
"cite_spans": [
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"start": 138,
"end": 158,
"text": "(Gliwa et al., 2019;",
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"text": "Zhu et al., 2021)",
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"section": "Introduction",
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"text": "Several dialogue corpora with associated humanwritten summaries have been created. These sum-maries differ significantly in type, style, and focus, depending on the instructions that were given to the human annotators. It is usually not clear why the particular summary type was chosen for the dialogue corpus at hand. In fact, we are not aware of any well-founded theory that answers this questions.",
"cite_spans": [],
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"section": "Introduction",
"sec_num": "1"
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"text": "To close this gap, we leverage the wellestablished linguistic model of dialogue types by Walton and Krabbe (1995) to identify suitable summary items for the different types of dialogues. This results in a combination of linguistically defined dialogue types, their features, and the suitable summary items. We then place all existing dialogue data sets with summaries that we are aware of into this matrix. This allows us to map the available resources and to identify gaps, which opens up directions for future work.",
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"text": "More precisely, this work presents four contributions:",
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"text": "1. A concise presentation of the linguistically grounded classification of dialogue types by Walton and Krabbe (Section 2) 2. A mapping from dialogue types to potential summary items and associated NLP tasks (Table 2). This indicates which summaries would be appropriate for which dialogue type.",
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"text": "3. An overview of all existing data sets for dialogue summarization that we are aware of (Section 3), which will be useful for researchers in the field even independent from the linguistic model. 4. A mapping from the existing data sets to the linguistic model, and an analysis of potential resource gaps (Section 4) .",
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"text": "We also present the overview of existing dialogue summarization data sets in a comprehensive tabular overview in Table A in the Appendix.",
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"section": "Introduction",
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"text": "The analysis of dialogues within linguistics is mainly investigated in the fields of conversation analysis and pragmatics. A large body of work investigates speech acts (Searle, 1969; Grice, 1975, inter alia) , i.e. dialogues are decomposed into individual turns and their communicative intents are analysed. A smaller body of work focuses on establishing a typology of dialogues. Among these works, Walton and Krabbe (1995) is a wellestablished model that is often cited and discussed.",
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"text": "Walton and Krabbe developed a model of dialogues types and their features which is often picked up in subsequent work in various fields. Table 1 ( upper part) shows the model. It features six basic dialogue types: Persuasion, Negotiation, Inquiry, Deliberation, Information-seeking, and Eristics. There are three additional mixed types: Debate (Persuasion and Eristics), Committee meeting (mainly Deliberation), and Socratic Dialogue (mainly Persuasion). 4 Recently, Macagno and Bigi (2018) showed how Walton and Krabbe's model is connected to theories of speech acts, dialect acts, and pragmatic acts and concepts such as \"communicative intentions\". Walton and Krabbe's dialogue types were explored by research in multi-agent communication in computer science. For example, Reed (1998) applies the model to derive dialogue frames to describe multi-agent interactions.",
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"text": "A related approach to dialogue type categorization is presented in Franke (2010 Franke ( , 2011 . The approach develops a taxonomy of (minimal) dialogues. Minimal dialogues are sequences of speech acts in a dialogue that have ended in a conclusion or decision. A naturally-occurring dialogue is then modelled as a sequence of these minimal dialogues.",
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"text": "It is noteworthy that naturally-occurring dialogues are seen as a mixture of multiple dialogue types in both aforementioned models. Still, we find that most of the dialogue corpora we examine in Section 3 can be assigned to one (or two) main dialogue type(s) under the Walton and Krabbe model. We choose the Walton and Krabbe model as the basis of our analysis of resources in the dialogue summarization space as it is generally the most established one and has been shown to extend well into other domains. Furthermore, the features that Walton and Krabbe attribute to the dialogue types enable us to infer desiderata for the type of summary that suits the dialogue type. For example, if the the main goal of a negotiation is \"making a deal\", then a suitable summary would present the deal that resulted from the negotiation. Similarly, if the main goal of a debate is \"accommodating conflicting points of views\", then a suitable summary would list these points of view, and by extension, attribute them to the speakers participating in the debate, and, going further, provide insight into the reasoning of the speakers etc. Finally, the generation of the desirable summary types can then be decomposed naturally into well-established NLP tasks such as topic detection, argument mining, and stance detection, etc.",
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"text": "In summary, the Walton and Krabbe model and its features provide a structured perspective on dialogues that lets us identify suitable connections between dialogue types and summary items, and enables us to pin-point NLP tasks that are applicable for accomplishing such summaries.",
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"text": "Having selected the model of dialogue types by Walton and Krabbe (1995) as the lens through which we wish to explore the resources in the dialogue summarization domain, we first infer desirable properties of summaries for each of the dialogue types. For this purpose, we examine the dialogue types' features (primarily: Initial situation and Main goal; secondarily: Participant's aim and Side benefits) to derive items that an optimal summary would contain in this view. To link the desirable summary items to specific NLP tasks, we note down NLP targets that need to be identified and extracted to enable a summarization system to produce the summary items in its outputs. The lower part of Table 1 presents the result of this process. 5 The summary items are ordered by importance in relation to our prioritization of the dialogue type features (i.e. Main goals are more important than Side benefits). We exemplify our mapping based on the Persuasion dialogue type: Walton and Krabbe (1995) , and their mapping to our proposed summary items (sorted by importance) and the applicable NLP tasks' target information.",
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"text": "The main goal of Persuasion dialogues is to revolve a conflict between multiple speakers. Each participant wants to persuade the others. For a summary, we are mainly interested in the different conflicting points of views (POV) and the resolution of the disagreement. However, the arguments used to resolve the conflict, and the final \"winner\" are also of interest. For each of these summary items, a corresponding NLP task can be used to extract a specific item. For instance, to extract the different POVs, stance detection can be applied. To extract the arguments used to persuade others, argument detection is applicable, etc. That is, summaries of a dialogue under a given dialogue type would ideally include these targets explicitly in a structured manner to facilitate the creation and evaluation of automatic summarization systems.",
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"text": "The list of all NLP targets emerging in the mapping are: Topics (tracking), Decisions/Action items, Arguments, Emotions/Sentiment, Stances, Keyfacts, and Knowledge. We will apply this inventory of NLP targets in Section 4 to map out existing resources and investigate which summary items have been explored for which dialogue types.",
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"text": "We next provide an overview of existing dialogue summarization data sets. The overview is complemented by Table A in the Appendix which offers a compact and comprehensive outline of the data sets including descriptions, sizes, covered languages, and available summary types. We divide the data sets into the domains that they cover (Meetings, Broadcast Interviews, Customer and Patient Support, Spontaneous Conversation) and discuss applicable dialogue types.",
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"text": "Dialogues can be either spoken or written. While several corpora of written or more formal dialogues and their summarization have emerged recently (Gliwa et al., 2019; Chen et al., 2021 , inter alia), we here focus on corpora for summarization of (transcripts of) spoken dialogues, which is considerably different than summarization of text, as described for example in Gurevych and Strube (2004) .",
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"text": "Work on summarizing spoken dialogues (i.e. involving more than one speaker) started in the late 1990s and early 2000s (see for example Zechner and Waibel (2000b,a) ). These already covered a great variety of different types of dialogues, such as TV discussions (NewsHour, CNN CrossFire), phone calls (CALLHOME, CALLFRIEND) and meetings. An overview of these early approaches into summarizing dialogues can be found in Zechner (2002) .",
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"text": "At the same time, the VERBMOBIL project, which focused on negotiations dialogues, also worked on summarising these (Reithinger et al., 2000; . 6",
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"text": "The topic of summarizing meetings gained considerable attraction with extensive work on the ICSI-Corpus (Morgan et al., 2001 ) and the AMI-Corpus (Murray et al., 2007, e.g.) . Murray et al. (2005) presented work on manually summarizing the ICSI meetings, where annotators were instructed to \"construct a textual summary [. . .] aimed at someone who is interested in the research being carried out\". Four headlines or questions served as guidelines: 1) Why are they meeting and what do they talk about? 2) Decisions made by the group, 3) progress and achievements and 4) problems described. Liu and Liu (2008) extended this work by creating more human summaries and evaluating the summaries based on a questionnaire to be filled out by humans. Other work looked in more detail into how to detect and summarize action items, their descriptions and their appropriate time frames (Purver et al., 2007, e.g.) .",
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"text": "The AMI corpus was also extensively studied in the context of summarization. However, while the ICSI corpus contains actual meetings of the participating research groups, which had a varied number of participants, the AMI corpus contains meetings of four persons with different roles in a product design scenario, which was not a natural scenario for the participants. Additionally, the topic is always the same, whereas the ICSI corpus has a wide variety of topics that were discussed in the meetings, including for example chit-chat among team members waiting for everyone to arrive. Summaries for the AMI corpus were created in an abstractive way, based on dialogue acts supporting the information in the summaries (Murray et al., 2007) . Fern\u00e1ndez et al. (2008) aimed at identifying \"decision-making sub-dialogues\" in the AMI meeting data. The authors state that a decision subdialogue consists of three components: a) an issue raised, b) proposals are considered and c) the decision. To that end, they annotate dialogue acts in the data that represent either the issue, or parts of the resolution and the decision.",
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"text": "Similar to the development in the text summarization domain, the dialogue summarization domain moved to using queries to represent the information need of a specific user (Mehdad et al., 2014) . Unfortunately, there was not data created for this scenario and the qualitative evaluation was performed on a small subset of the data.",
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"text": "Wang and Cardie (2012) and Wang and Cardie (2013) also work on summarizing meetings, but rather than aiming for a generic summary, they present work on summarizing focused summaries, that are based on specific aspects of a meeting, such as decisions, action items etc.",
"cite_spans": [],
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"text": "Following in the footsteps of the AMI corpus Yamamura et al. (2016) present a similar dataset for the Japanese language named \"Kyutech Corpus\", which also includes reference summaries created in the same fashion as the reference summaries for the AMI corpus.",
"cite_spans": [],
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"text": "More recently, Zhong et al. (2021) used queries to represent information need when accessing the ICSI and AMI corpora.",
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"text": "Another type of meeting dialogues occur in the political domain. Political debates from the UK's House of Commons have been used by Vilares and He (2017) . The authors aim to produce summaries which give a brief overview on the main viewpoints exchanged and perspectives expressed, which puts it in the area of stance classification and argument mining.",
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"text": "Committee meetings from the Welsh and Canadian Parliament are used by Zhong et al. (2021) . Their aim is to create informative summaries based on two types of queries: General queries and specific queries, which included discussion points, opinions, ideas etc. In the discussions elements relevant to the queries have been annotated, as well as informative summaries created.",
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"text": "Dialogue Types The discussed corpora in the meeting domain mainly cover project, team, and committee meetings. Given the Initial situation settings of Need for action, conflict of interest & need for cooperation, and the Main goals Reach a decision, Making a deal, we assign this domain to the dialogue types Deliberation and Negotiation.",
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"text": "TV discussions were already studied in the early phases of speech summarization. More recent work is presented by Zhu et al. (2021) based on NPR and CNN interviews. Reference summaries are based on the descriptions of the interviews and the list of topics discussed.",
"cite_spans": [
{
"start": 114,
"end": 131,
"text": "Zhu et al. (2021)",
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"section": "Broadcast Interviews",
"sec_num": "3.2"
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"text": "Podcasts are another form of exchange, that can be an interview, but it can also be a discussion. Clifton et al. (2020) present a data set of Podcasts used for summarization. Reference summaries are based on creator-generated descriptions, which are most likely rather indicative than informative. Using generic summarization algorithms, summaries are created automatically and evaluated manually.",
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"text": "Clifton et al. (2020)",
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"section": "Broadcast Interviews",
"sec_num": "3.2"
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"text": "Dialogue Types While the formats covered in the corpora in this domain are rather open by nature, we map it to the dialogue types Informationseeking, e.g. interviews with an experts where ignorance (Initial situation) is remedied by the expert's knowledge (Main goal), and Debate, where the Initial situation is the presence of conflicting views that are accommodated and discussed in front of an audience (Main goal).",
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"section": "Broadcast Interviews",
"sec_num": "3.2"
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"text": "Early work in dialogue summarization also includes call-center dialogues. Higashinaka et al. (2010) present work in this direction, which is unfortunately not based on actual call-center dialogues, but rather on recordings of people who were assigned various roles. Tamura et al. (2011) improved on this by using actual call center data. As the logs available for each dialogue were deemed unsuitable for summarization, two types of summaries were created: 1) Indicative summaries, for agents or managers to grasp the gist of the calls and 2) Informative summaries, that contain the content and allow managers to get necessary details of the calls.",
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"text": "Tamura et al. (2011)",
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"section": "Customer and Patient Support",
"sec_num": "3.3"
},
{
"text": "Favre et al. (2015) also present work on summarizing call center dialogues. The aim is to create synopses of the calls, which contain the problem and the suggested solution. As opposed to most other work presented, the data set covered not only English, but French (Decoda Corpus) and Italian (Luna Corpus). Based on the same data sets Danieli et al. (2016) looks into analysing the behavior shown in the conversation, which is an important aspect for quality assurance supervisors. Liu et al. (2019) present work on the DiDicorpus, which contains dialogues from customer service centers and summaries created by the respective agents. Their aim is to identify key-point sequences in the dialogues, to which end they devise a tagging system with 51 labels, ranging from \"Question Description\" to \"Solution\". Zhao et al. (2020) present work on the Automobile Master Corpus, which contains data from a customer question and answer scenario. It is unclear what the summaries are aimed at, so we have to assume that they are generic summaries.",
"cite_spans": [
{
"start": 336,
"end": 357,
"text": "Danieli et al. (2016)",
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"start": 483,
"end": 500,
"text": "Liu et al. (2019)",
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"start": 808,
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"text": "Zhao et al. (2020)",
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"section": "Customer and Patient Support",
"sec_num": "3.3"
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"text": "Various data sets have been used for summarization that come from the medical domain. Acharya et al. (2019) present work on a data set where patients with a specific condition are interviewed. As the data contains actual interviews it cannot be shared. The summaries created aim to include sentences that motivate patients to get better. Joshi et al. (2020) and Yim and Yetisgen (2021) work on a data set of medical interviews where reference summaries are created by medical doctors, instructing them to summarize as they would for a \"clinical note by including all the relevant information\". A specific focus was put on negative utterances such as \"does not have symptom X\".",
"cite_spans": [
{
"start": 86,
"end": 107,
"text": "Acharya et al. (2019)",
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"start": 338,
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"text": "Joshi et al. (2020)",
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"sec_num": "3.3"
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"text": "Dialogue Types This domain clearly evolves around the need for specific information exchange (Initial setting) and passing knowledge between the speakers (Main goal). We thus assign it the Information-seeking dialogue type.",
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"section": "Customer and Patient Support",
"sec_num": "3.3"
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"text": "Spontaneous or rather informal conversations were already part of the early work presented by Zechner and Waibel (2000b) and Zechner and Waibel (2000a) , which looked at the CALLHOME and CALLFRIEND data, which consists of telephone conversations.",
"cite_spans": [
{
"start": 94,
"end": 120,
"text": "Zechner and Waibel (2000b)",
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"start": 125,
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"text": "Zechner and Waibel (2000a)",
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"section": "Spontaneous Conversations",
"sec_num": "3.4"
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"text": "A similar setting is the basis for the Switchboard Corpus, which also contains telephone con-versations on specific topics. Gurevych and Strube (2004) required annotators to \"select the most important utterances\" in a selection of dialogues and formed two types of gold standard: One based on all three annotators and one based on annotations by at least two annotators.",
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"start": 124,
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"text": "Gurevych and Strube (2004)",
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"section": "Spontaneous Conversations",
"sec_num": "3.4"
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"text": "A more recent type of informal dialogues has been presented by Rameshkumar and Bailey (2020) which contains dialogues in the context of pen and paper role-playing games (CD3 data set). Summaries are provided through a wiki and are produced by fans of the associated show.",
"cite_spans": [
{
"start": 63,
"end": 92,
"text": "Rameshkumar and Bailey (2020)",
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"section": "Spontaneous Conversations",
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"text": "Dialogue Types This domain is difficult to assert in terms of dialogue types as the features Initial situation and Main goal are not clearly identifiable. While speakers were given a specific topic for a conversation in most cases, they were not specifically instructed to converse in a predefined manner. We can hence only speculate on the dialogue types mirrored in these conversations; the conversations would have to be examined individually to determine a sequence of matching dialogue types, which is infeasible in our study.",
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"section": "Spontaneous Conversations",
"sec_num": "3.4"
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"text": "Given the overview of dialogue summarization resources and their mapping to dialogue types under the linguistic model in the previous section, and the summary items assigned to the dialogue types in Section 2.2, we are now able to tabulate the corpora and the summary items 7 to see what areas in this space are covered and where there are opportunities for future work.",
"cite_spans": [],
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"section": "Mapping Data Sets to Summary Items",
"sec_num": "4"
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"text": "Specifically, we tabulate corpora and the NLP targets that are mapped to the summary items and insert the paper references that cover the summary item for a given corpus. We perform this mapping under the requirement that a resource explicitly annotates a given NLP target in a structured manner. That is, while a general, abstractive, manual summary of a meeting might include e.g. action items or decisions, they might not be marked explicitly as such in the summaries or the underlying transcripts. In such a setting, the resource would not enable the creation of summarization systems that explicitly extract e.g. action items. 8 Table 2 shows the result of this mapping. A quick glance reveals that only a small portion of the potential NLP targets are explicitly annotated in the summarization resources. The table also shows where efforts to create resources have been focused in the dialogue summarization space: The corpora in all domains mainly offer topics-related summaries. The meetings domain is an exception, where considerable effort has been put into annotating decisions and action items.",
"cite_spans": [],
"ref_spans": [
{
"start": 634,
"end": 641,
"text": "Table 2",
"ref_id": "TABREF3"
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"section": "Mapping Data Sets to Summary Items",
"sec_num": "4"
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{
"text": "Early approaches to create resources for dialogue summarization in the 2000's were based on spontaneous conversations. Such dialogues are difficult to map to the Walton and Krabbe types, as the features instantiations, such as Initial Situation or Main Goal, are hard to determine. The diversity of these conversations also makes it difficult to define clear guidelines for creating summaries: Annotators were mostly guided by a somewhat under-specified relevancy criterion and were given a length constraint. In regards to the covered summary items, such extractive summaries might contain e.g. decisions and stances etc., however, they are not marked or labeled in the extracted dialogue segments explicitly.",
"cite_spans": [],
"ref_spans": [],
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"section": "Discussion",
"sec_num": "5"
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{
"text": "In the Meetings domain, summarization efforts became more specific and a substantial body of work looked into decisions and action items, which resulted in structured data sets for these summary items. For other summary items that the dialogue types Negotiation and Deliberation yield, such as Stances and Arguments, no structured resources exist, however.",
"cite_spans": [],
"ref_spans": [],
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"section": "Discussion",
"sec_num": "5"
},
{
"text": "Available summaries in the Broadcast domain consist of content description by the authors/creators of the content, i.e. they were not created by researchers for the purpose of dialogue summarization. The descriptions thus rather follow the (potentially commercially-motivated) goal of raising interest in a audience, rather than providing an informative or indicative summary. The communicative intent of such descriptions can therefore be considered to be substantially different from that of research-oriented summarization data sets. Naturally, such content descriptions do not explicitly make available any specific summary items.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "5"
},
{
"text": "the most important topics are summarized for all dialogues, but the queries that cover decisions are not guaranteed to be present for all dialogues and are not explicitly labeled as being related to decisions. In the customer and patient support domain, summarization efforts also leveraged readily available resources such as synopses of call logs or doctor's notes as the summarization targets. Here, the goal of summarization efforts can be mainly described as automating the task of manually producing such notes or synopses. Hence, many linguistically motivated summary items that our approach yields for the Information-seeking dialogue type may sim-ply not apply to the particular use cases that are covered by the existing resources, and are thus not marked explicitly as such.",
"cite_spans": [],
"ref_spans": [],
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"section": "Discussion",
"sec_num": "5"
},
{
"text": "We have provided an overview of existing corpora in the domain of spoken dialogue summarization. We found that topic-related extractive or abstractive summaries are predominant, and are often guided by high-level criteria, i.e. summary guidelines ask for content of \"high relevancy\" to be included without further specifications.",
"cite_spans": [],
"ref_spans": [],
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"section": "Conclusion",
"sec_num": "6"
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{
"text": "Furthermore, we have applied a linguistically motivated view on dialogues to the available corpora that yields more specific summary items, such as arguments, stances, or emotions. We found that such specific items are scarcely available in a structured manner in existing corpora. As there are several resources available for e.g. argument mining (Lawrence and Reed, 2020) and stance detection (K\u00fc\u00e7\u00fck and Can, 2020) in dialogues, a potential direction for future work could be an effort to bring together such resources.",
"cite_spans": [
{
"start": 348,
"end": 373,
"text": "(Lawrence and Reed, 2020)",
"ref_id": "BIBREF15"
},
{
"start": 395,
"end": 416,
"text": "(K\u00fc\u00e7\u00fck and Can, 2020)",
"ref_id": "BIBREF14"
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],
"ref_spans": [],
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"section": "Conclusion",
"sec_num": "6"
},
{
"text": "While our model-driven view on the dialogue summarization space might be insightful and fruitful for future research, it should not be understood in a normative way: it is not intended to point out that certain directions are misguided. For instance, although our mapping does not yield Emotion as a summary item for Negotiation dialogues, there might be relevant use cases for this line of inquiry. Neither does the approach have any claim to completeness in terms of meeting the information needs of different users. In this regard, querybased approaches seem to hold a large potential to cover a wide variety of information needs (Zhong et al., 2021) . However, since summary items are seamlessly embedded in the natural-language responses in such settings, it is uncertain how well query-based methods are able to generate on-thefly responses for realistic queries like \"what are the action items assigned to me and by when do I have to complete them?\". Answering such information needs robustly seems to necessitate that the underlying information is extracted in a structured manner (Purver et al., 2007, e.g.) to be able to generate an appropriate and complete response.",
"cite_spans": [
{
"start": 633,
"end": 653,
"text": "(Zhong et al., 2021)",
"ref_id": "BIBREF39"
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{
"start": 1089,
"end": 1116,
"text": "(Purver et al., 2007, e.g.)",
"ref_id": null
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"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Overall, our analysis indicates that the question of what are appropriate summaries of dialogues is a challenging one, and we have presented a view that offers some answers. While emerging querybased approaches seem to be a fruitful direction due to their potential to cover a high variety of information needs, we believe that linguistic considerations, as those outlined in this work, should also be leveraged to support resource creation efforts in the dialogue summarization space in future work.",
"cite_spans": [],
"ref_spans": [],
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"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Future work should also evaluate the summaries resulting from more dialogue-specific annotations as opposed to generic summaries especially with respect to the individual information needs of various users. This of course also leads to developing methods that take the information need into account when creating such summaries automatically.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Telemedicine 25,000 conversations from a telemedicine platform.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "-Medical doctors were asked to summarize the sections of 3000 snippets as they would for a typical clinical note by including all the relevant information (Joshi et al., 2020) .",
"cite_spans": [
{
"start": 155,
"end": 175,
"text": "(Joshi et al., 2020)",
"ref_id": "BIBREF13"
}
],
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"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "Clinical Encounter Visits Audio and clinical notes from clinical encounter visits from 500 visits and 13 providers.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "-Clinical notes as summary of the patient visit (Yim and Yetisgen, 2021) .",
"cite_spans": [
{
"start": 48,
"end": 72,
"text": "(Yim and Yetisgen, 2021)",
"ref_id": "BIBREF34"
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],
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"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "Spontaneous Conversation Corpora. Dialogue types: N/A",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "Spontaneous telephone conversations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Callhome corpus",
"sec_num": null
},
{
"text": "-For 9 English and 14 Spanish dialogues, the most relevant turns were marked (Zechner and Waibel, 2000b) .",
"cite_spans": [
{
"start": 77,
"end": 104,
"text": "(Zechner and Waibel, 2000b)",
"ref_id": "BIBREF37"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "EN, ES",
"sec_num": null
},
{
"text": "Four audio excerpts from four television shows.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Televison shows",
"sec_num": null
},
{
"text": "-Most relevant, meaningful, concise, and informative phrases (Zechner and Waibel, 2000a) .",
"cite_spans": [
{
"start": 61,
"end": 88,
"text": "(Zechner and Waibel, 2000a)",
"ref_id": "BIBREF36"
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],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "Switchboard Telephone conversations of at least 10 minutes duration on a given topic. \u223c2000 turns.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "-10% of all utterances in the dialogue marked as being relevant (Gurevych and Strube, 2004) .",
"cite_spans": [
{
"start": 64,
"end": 91,
"text": "(Gurevych and Strube, 2004)",
"ref_id": "BIBREF11"
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],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "Combination of English learner corpora and dialogue understanding data sets. 13,460 dialogues.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DialogSum",
"sec_num": null
},
{
"text": "(1) convey the most salient information; (2) be brief (no longer than 20% of the conversation); (3) preserve important named entities within the conversation; (4) be written from an observer perspective; (5) be written in formal language (Chen et al., 2021) .",
"cite_spans": [
{
"start": 238,
"end": 257,
"text": "(Chen et al., 2021)",
"ref_id": "BIBREF2"
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],
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"section": "EN -",
"sec_num": null
},
{
"text": "Transcripts of Dungeons and Dragons role-playing game. 398,682 turns.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CRD3",
"sec_num": null
},
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"text": "-Multiple summaries available, e.g. an abstract of the resulting plot/narrative of a game. Includes abstractive summaries collected from the Fandom wiki (Rameshkumar and Bailey, 2020) . Table 3 : Overview of existing dialogue summarization data sets. The last column lists papers that provide manually created summaries for a given corpus.",
"cite_spans": [
{
"start": 153,
"end": 183,
"text": "(Rameshkumar and Bailey, 2020)",
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{
"start": 186,
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"text": "https://elitr.github.io/ automatic-minuting/index.html 2 https://elitr.github.io/ automatic-minuting/summdial.html3 In this paper we focus on spontaneous spoken dialogues, leaving out written dialogues such as Twitter discussion, scripted dialogues, which occur for example in movies, and the summarization of material spoken by single persons only.",
"cite_spans": [],
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"section": "",
"sec_num": null
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"text": "We omit the mixed dialogue types inTable 1for brevity, as they are combinations of the other types.",
"cite_spans": [],
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"section": "",
"sec_num": null
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"text": "To encourage different takes in this mapping process, the authors of this paper individually performed the task of mapping dialogue types to summary items and NLP tasks and then held a discussion to harmonize the mappings. Overall, the mappings of the authors overlapped to a large extent and complemented each other, i.e., no conflicting points or disagreement emerged",
"cite_spans": [],
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"section": "",
"sec_num": null
},
{
"text": "Note that in the following we do not present all existing work in the domain of dialogue summarization, but focus on those that present representative research results or annotations.",
"cite_spans": [],
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"section": "",
"sec_num": null
},
{
"text": "We omit the Knowledge summary item, since no resource covers it. However, Knowledge Discovery might be an interesting task in Inquiry dialogues.8 However, it is not always straight-forward to apply the NLP targets to resources. For example, in the QMSum corpus,",
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"back_matter": [
{
"text": " (Reithinger et al., 2000) .-Agreements on scheduling, accommodation, traveling, entertainment. .ICSI Corpus Informal, natural, and even impromptu meetings at ICSI. 38 meetings for a total of 39 hours, transcribed about 12 hours. 237 participants, 49 unique speakers.",
"cite_spans": [
{
"start": 1,
"end": 26,
"text": "(Reithinger et al., 2000)",
"ref_id": "BIBREF26"
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"section": "annex",
"sec_num": null
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"text": "-Summaries answering the following questions: Why are they meeting and what do they talk about? Decisions made by the group? Progress and achievements? Problems described (Murray et al., 2005 ).-Find dialogue acts that relate to action items (descriptions, time frames, owners, agreements) (Purver et al., 2007) .-Abstract summarizing each important output for every meeting. Decision and problem summaries are annotated (Wang and Cardie, 2013) .AMI Corpus 100 hours of meeting recordings.",
"cite_spans": [
{
"start": 171,
"end": 191,
"text": "(Murray et al., 2005",
"ref_id": "BIBREF22"
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{
"start": 290,
"end": 311,
"text": "(Purver et al., 2007)",
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"start": 421,
"end": 444,
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"ref_id": "BIBREF32"
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"section": "EN",
"sec_num": null
},
{
"text": "-Ranking the dialogue acts in terms of being extract-worthy (Murray et al., 2007) .-Classify utterances related to decisions: issue (I), resolution (R), and agreement (A). Two authors annotated 9 and 10 dialogues each (Fern\u00e1ndez et al., 2008) .-An abstract summarizing each decision; dialogue acts that support each decision are annotated (Wang and Cardie, 2012 ).-Abstract summarizing each important output for every meeting. Decision and problem summaries are annotated (Wang and Cardie, 2013) .",
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{
"start": 60,
"end": 81,
"text": "(Murray et al., 2007)",
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"start": 218,
"end": 242,
"text": "(Fern\u00e1ndez et al., 2008)",
"ref_id": "BIBREF6"
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"start": 339,
"end": 361,
"text": "(Wang and Cardie, 2012",
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"start": 472,
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"sec_num": null
},
{
"text": "A decision-making task in a virtual shopping mall in a virtual city. 9 conversations. ",
"cite_spans": [],
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"eq_spans": [],
"section": "Kyutech Corpus",
"sec_num": null
},
{
"text": "-Creator-generated descriptions as reference summaries (Clifton et al., 2020) .Customer & Patient Support Corpora. Dialogue types: Information-seeking",
"cite_spans": [
{
"start": 55,
"end": 77,
"text": "(Clifton et al., 2020)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "Logs in the DiDi (mobile transportation platform) customer service center.",
"cite_spans": [],
"ref_spans": [],
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"section": "DiDi",
"sec_num": null
},
{
"text": "-Abstractive summaries written by agents. \u223c300k pairs of dialogues and summaries. \"Key point sequences\", i.e. a set of 51 a set action/decision items are also annotated (Liu et al., 2019) .Call center I Simulated contact center dialogues in six domains. 15-20 tasks per domain. \u223c700 dialogues.",
"cite_spans": [
{
"start": 169,
"end": 187,
"text": "(Liu et al., 2019)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "EN",
"sec_num": null
},
{
"text": "-Scenario texts used as reference data (Higashinaka et al., 2010) . ",
"cite_spans": [
{
"start": 39,
"end": 65,
"text": "(Higashinaka et al., 2010)",
"ref_id": "BIBREF12"
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],
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"section": "JP",
"sec_num": null
},
{
"text": "-Abstractive summaries about the main events of the conversations, such as the objective of the caller, whether and how it was solved by the agent, and the attitude of both parties. Synopses written by quality assurance experts from call centres (Favre et al., 2015) .",
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{
"start": 246,
"end": 266,
"text": "(Favre et al., 2015)",
"ref_id": "BIBREF5"
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"BIBREF0": {
"ref_id": "b0",
"title": "A quantitative analysis of patients' narratives of heart failure",
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{
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"middle": [],
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{
"first": "Barbara",
"middle": [
"Di"
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"last": "Eugenio",
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{
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