| { | |
| "paper_id": "O18-1008", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:09:53.149840Z" | |
| }, | |
| "title": "An Ensemble Approach for Multi-document Summarization using Genetic Algorithms", | |
| "authors": [ | |
| { | |
| "first": "Chun-Chang", | |
| "middle": [], | |
| "last": "\u9673\u4fca\u7ae0", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Yuan Ze University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yu-Hang", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Yuan Ze University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Cheng-Zen", | |
| "middle": [], | |
| "last": "Chung", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Yuan Ze University", | |
| "location": {} | |
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| "email": "" | |
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| "first": "\u5143\u667a\u5927\u5b78\u8cc7\u8a0a\u5de5\u7a0b\u5b78\u7cfb", | |
| "middle": [], | |
| "last": "Yang", | |
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| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Yuan Ze University", | |
| "location": {} | |
| }, | |
| "email": "[email protected]" | |
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| { | |
| "first": "Jhih-Sheng", | |
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| "laboratory": "", | |
| "institution": "Academia Sinica", | |
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| "last": "Fan", | |
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| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Academia Sinica", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chao-Yuan", | |
| "middle": [], | |
| "last": "\u674e\u8087\u5143", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "\u598d\u767c\u79d1\u6280\u80a1\u4efd\u6709\u9650\u516c\u53f8", | |
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| "last": "Lee", | |
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| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Multi-document summarization is an important research task in text summarization. It helps people to reduce much time in reading articles of similar contents but with the same topics. In this study, we propose an ensemble model based on genetic algorithms. Two ensemble summarization models are thus", | |
| "pdf_parse": { | |
| "paper_id": "O18-1008", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "Multi-document summarization is an important research task in text summarization. It helps people to reduce much time in reading articles of similar contents but with the same topics. In this study, we propose an ensemble model based on genetic algorithms. Two ensemble summarization models are thus", | |
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| "section": "Abstract", | |
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| "body_text": [ | |
| { | |
| "text": "constructed, one for four network summarization models, and the other for four probabilistic topic network models. These two ensemble models use genetic algorithms to find the optimal weights. We use the datasets of DUC 2004 to DUC 2007 for performance evaluation. The experimental results show that these two ensemble models can achieve the best performance in ROUGE-1, ROUGE-2, and ROUGE-SU4 than other standalone network models and standalone probabilistic topic network models. ", | |
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| "ref_entries": { | |
| "TABREF0": { | |
| "num": null, | |
| "text": "\u95dc\u9375\u5b57\uff1a\u591a\u6587\u4ef6\u6458\u8981\uff0c\u7d44\u5408\u6458\u8981\u6a21\u578b\uff0c\u57fa\u56e0\u6f14\u7b97\u6cd5\uff0c\u6548\u80fd\u8a55\u4f30 Keyword: Multi-document summarization, ensemble summarization models, genetic algorithms, performance evaluation \u4e00\u3001 \u7dd2\u8ad6 \u6587\u4ef6\u6458\u8981 (Text Summarization) \u6280\u8853\u80fd\u5f9e\u6587\u4ef6\u4e2d\u64f7\u53d6\u51fa\u91cd\u8981\u8cc7\u8a0a\u5f59\u6574\u6210\u6458\u8981\uff0c\u662f\u76f8 \u7576\u53d7\u5230\u91cd\u8996\u7684\u7814\u7a76[7,11]\u3002\u4f9d\u7167\u8655\u7406\u7684\u6587\u4ef6\u6578\u91cf\uff0c\u6587\u4ef6\u6458\u8981\u6280\u8853\u53ef\u5206\u70ba\u55ae\u6587\u4ef6\u6458\u8981(Single Document Summarization)\u6280\u8853[4]\u8207\u591a\u6587\u4ef6\u6458\u8981(Multi-document Summarization) \u6280\u8853 [1,2,3,5,9,13,14,15]\u3002\u8fd1\u5e74\u4f86\u8a31\u591a\u7814\u7a76\u90fd\u5728\u7814\u8a0e\u591a\u6587\u4ef6\u6458\u8981\u6280\u8853\u7684\u767c\u5c55\u3002\u900f\u904e\u5c07\u8a31\u591a\u76f8\u95dc \u7684\u6587\u4ef6\u9032\u884c\u5206\u6790\uff0c\u7522\u751f\u7cbe\u7c21\u7684\u6458\u8981\uff0c\u80fd\u5e6b\u52a9\u4eba\u5011\u5feb\u901f\u4e86\u89e3\u91cd\u8981\u8cc7\u8a0a\u3002\u5728\u904e\u5f80\u591a\u6587\u4ef6\u6458\u8981 \u7814\u7a76\u4e2d\uff0c\u5df2\u63d0\u51fa\u8a31\u591a\u65b9\u6cd5\u3002\u4f8b\u5982MEAD \u4f7f\u7528Centroid-based Summarization \u7684\u6280\u8853\uff0c\u4f86 \u8a08\u7b97\u8a9e\u53e5\u7684\u91cd\u8981\u6027[9]\u3002Xiong\u8207Luo\u63d0\u51fa\u4ee5Latent Semantic Analysis (LSA)\u7684\u6280\u8853\u4f86\u63d0\u5347", | |
| "content": "<table><tr><td>\u7d44\u5408\u5f0f\u591a\u6587\u4ef6\u6458\u8981\u6a21\u578b\u5247\u6c92\u6709\u8003\u616e\u4f7f\u7528\u8005\u9700\u6c42\uff0c\u662f\u901a\u7528\u578b\u7684\u591a\u6587\u4ef6\u6458\u8981\u65b9\u5f0f\u3002 \u5011\u5229\u7528\u7db2\u8def\u6a21\u578b\u8a08\u7b97\u8a9e\u53e5\u91cd\u8981\u6027\u3002\u5728\u7db2\u8def\u6a21\u578b\u4e2d\uff0c\u6bcf\u500b\u7bc0\u9ede\u4ee3\u8868\u4e00\u500b\u8a9e\u53e5\u3002\u7bc0\u9ede\u4e4b\u9593\u7684</td></tr><tr><td>\u70ba\u4e86\u8a55\u4f30\u6240\u63d0\u51fa\u7684\u7d44\u5408\u5f0f\u591a\u6587\u4ef6\u6458\u8981\u6a21\u578b\uff0c\u6211\u5011\u4f7f\u7528DUC (Document Understanding \u9023\u7d50\uff0c\u5247\u7531\u8a9e\u53e5TF-IDF \u5411\u91cf\u7684Cosine Similarity \u4f86\u6c7a\u5b9a\u3002\u5982\u679c\u76f8\u4f3c\u5ea6\u8d85\u904e\u4e00\u500b\u9580\u6abb\u503c\uff0c</td></tr><tr><td>Conference) 2004\u5e74\u81f32007\u5e74\u56db\u5e74\u7684\u8cc7\u6599\u96c6\u9032\u884c\u5be6\u9a57\u3002\u5728\u4ee5\u56db\u500b\u7db2\u8def\u6458\u8981\u6a21\u578b(Degree \u5247\u5169\u500b\u8a9e\u53e5\u7bc0\u9ede\u4e4b\u9593\u5b58\u5728\u9023\u7d50\u95dc\u4fc2\uff0c\u5efa\u69cb\u51fa\u6587\u7ae0\u8a9e\u53e5\u4e4b\u9593\u7684\u95dc\u4fc2\u7db2\u8def\u3002Degree Centrality</td></tr><tr><td>Centrality [3]\uff0cNormalized Similarity-based Degree Centrality [15]\uff0cPageRank Centrality [3]\uff0c \u65b9\u6cd5\u6703\u8a08\u7b97\u6bcf\u500b\u7bc0\u9ede\u7684\u5c0d\u5916\u9023\u7d50\u6578\uff0c\u5c0d\u5916\u9023\u7d50\u6578\u8d8a\u591a\u7684\u7bc0\u9ede\u4ee3\u8868\u8d8a\u91cd\u8981\u3002\u8a08\u7b97\u65b9\u5f0f\u5982\u5f0f</td></tr><tr><td>iSpreadRank Centrality [14])\u69cb\u6210\u7684\u7d44\u5408\u6458\u8981\u6a21\u578b\u4e2d\uff0c\u7121\u8ad6\u662f\u5728ROUGE-1\uff0cROUGE-2\u6216 (1)\uff0c\u5176\u4e2d\u03b1\u70ba\u76f8\u4f3c\u5ea6\u9580\u6abb\u503c\uff0c \u662f\u8a9e\u53e5\uff0cdeg( )\u662f \u7684Degree Centrality\uff1a</td></tr><tr><td>ROUGE-SU4\u9019\u4e9b\u8a55\u5206\u9805\u76ee\u4e0a\uff0c\u76f8\u8f03\u65bc\u500b\u5225\u55ae\u4e00\u6458\u8981\u6a21\u578b\u90fd\u80fd\u5920\u5f97\u5230\u6700\u597d\u7684\u8868\u73fe\u3002\u5728\u4ee5 deg( ) = \u2211 1 : \u2260 ( , )\u2265 (1)</td></tr><tr><td>\u56db\u500b\u6a5f\u7387\u4e3b\u984c\u7db2\u8def\u6458\u8981\u6a21\u578b(PL-Degree\uff0cPL-NSDC\uff0cPL-PageRank\uff0cPL-iSpreadRank)[13] \u4f46\u662f\u7576\u4e00\u4e9b\u4e0d\u91cd\u8981\u7684\u7bc0\u9ede\u56e0\u5f7c\u6b64\u9023\u7d50\u800c\u6709\u9ad8\u7684\u9023\u7d50\u6578\u6642\uff0cDegree Centrality\u6703\u5c07\u9019\u4e9b\u4e0d</td></tr><tr><td>\u69cb\u6210\u7684\u7d44\u5408\u6458\u8981\u6a21\u578b\u4e2d\uff0c\u5728ROUGE-1\uff0cROUGE-2\u6216ROUGE-SU4\u9019\u4e9b\u8a55\u5206\u9805\u76ee\u4e0a\uff0c\u76f8\u8f03 \u91cd\u8981\u7684\u8a9e\u53e5\u4e5f\u7d0d\u5165\u6458\u8981\uff0c\u964d\u4f4e\u6458\u8981\u7d50\u679c\u7684\u54c1\u8cea\u3002\u56e0\u6b64Erkan\u7b49\u4eba\u5229\u7528\u7db2\u8def\u6a21\u578bPageRank</td></tr><tr><td>\u65bc\u5404\u5225\u6a5f\u7387\u4e3b\u984c\u7db2\u8def\u6458\u8981\u6a21\u578b\uff0c\u4e5f\u80fd\u5920\u5f97\u5230\u6700\u597d\u7684\u8868\u73fe\u3002 \u4f86\u8a08\u7b97\u51fa\u8a9e\u53e5\u7684\u91cd\u8981\u6027\u3002\u56e0\u6b64\u5c0d\u65bc \u7684PageRank\u5206\u6578 ( )\uff0c\u8a08\u7b97\u65b9\u5f0f\u5982\u4e0b\uff1a</td></tr><tr><td>\u672c\u8ad6\u6587\u5176\u9918\u5167\u5bb9\u5b89\u6392\u5982\u4e0b\uff1a\u7b2c\u4e8c\u7bc0\u5c07\u4ecb\u7d39\u591a\u6587\u4ef6\u6458\u8981\u7684\u76f8\u95dc\u7814\u7a76\u3002\u7b2c\u4e09\u7bc0\u5c07\u8aaa\u660e\u57fa</td></tr><tr><td>\u65bc\u57fa\u56e0\u6f14\u7b97\u6cd5\u7684\u7d44\u5408\u5f0f\u591a\u6587\u4ef6\u6458\u8981\u6a21\u578b\u7684\u8a2d\u8a08\u3002\u7b2c\u56db\u7bc0\u5c07\u8aaa\u660e\u4ee5DUC2004\u5e74\u81f32007\u5e74</td></tr><tr><td>\u56db\u500b\u8cc7\u6599\u96c6\u9032\u884c\u5be6\u9a57\u7684\u7d50\u679c\u3002\u6700\u5f8c\uff0c\u7b2c\u4e94\u7bc0\u662f\u672c\u8ad6\u6587\u7684\u7d50\u8ad6\u3002</td></tr><tr><td>\u4e8c\u3001 \u76f8\u95dc\u7814\u7a76</td></tr><tr><td>\u5728\u904e\u5f80\u591a\u6587\u4ef6\u6458\u8981\u6280\u8853\u76f8\u95dc\u7814\u7a76\u4e0a\uff0cRadev\u7b49\u4eba\u57282000\u5e74\u63d0\u51faMEAD\u591a\u6587\u4ef6\u6458\u8981\u5668</td></tr><tr><td>[9]\u3002\u4ed6\u5011\u4f7f\u7528Centroid-based Summarization (CBS) \u8cea\u5fc3\u8a08\u7b97\u6280\u8853\uff0c\u5229\u7528\u4e00\u500b\u4e3b\u984c\u5075\u6e2c\u8ffd</td></tr><tr><td>\u591a\u6587\u4ef6\u6458\u8981\u7684\u6548\u80fd[12]\u3002Erkan\u8207Radev\u63d0\u51faLexRank[3]\uff0c\u4ee5PageRank\u7684\u7db2\u8def\u6a21\u578b\u8a08\u7b97\u8a9e \u8e64(Topic Detection and Tracking, TDT) \u7684\u96c6\u7fa4\u8a08\u7b97\u65b9\u6cd5\u627e\u51fa\u6587\u4ef6\u96c6\u7684\u8cea\u5fc3\u3002\u4ed6\u5011\u5c07</td></tr><tr><td>\u53e5\u91cd\u8981\u6027\u3002Yang\u7b49\u4eba\u4ee5Probabilistic Latent Semantic Analysis (PLSA)\u8a08\u7b97\u8a9e\u53e5\u4e3b\u984c\u4e26\u7d50 MEAD\u7684\u8cea\u5fc3\u503c\u8207Positional value \u548cFirst-sentence overlap\u4e09\u7a2e\u7279\u5fb5\u8403\u53d6\u51fa\u7684\u6458\u8981\u7d93\u904e\u5be6</td></tr><tr><td>\u5408\u7db2\u8def\u6a21\u578b\u4f86\u8a08\u7b97\u8a9e\u53e5\u91cd\u8981\u6027[13]\u3002 \u9a57\u6bd4\u8f03\u5f8c\uff0c\u5be6\u9a57\u7d50\u679c\u986f\u793a\u8cea\u5fc3\u503c\u7684\u6548\u80fd\u6700\u597d\u3002</td></tr><tr><td>\u7136\u800c\u904e\u5f80\u7814\u7a76\u5927\u591a\u53ea\u96c6\u4e2d\u8a0e\u8ad6\u5728\u591a\u6587\u4ef6\u6458\u8981\u6a21\u578b\u4e2d\u5982\u4f55\u4f7f\u7528\u67d0\u4e00\u985e\u578b\u7279\u5fb5\u4e2d\u54ea\u4e00 \u6f5b\u5728\u8a9e\u7fa9\u5206\u6790(Latent Semantic Analysis, LSA)\u4e5f\u66fe\u88ab\u4f7f\u7528\u5728\u591a\u6587\u4ef6\u6458\u8981\u6280\u8853\u4e2d\u3002\u4f8b</td></tr><tr><td>\u7a2e\u7279\u5fb5\u80fd\u88ab\u4f7f\u7528\u4f86\u5206\u6790\u9019\u4e9b\u6587\u7ae0\u7684\u8a9e\u53e5\u91cd\u8981\u6027\uff0c\u6c92\u6709\u8003\u616e\u5982\u4f55\u7d9c\u5408\u9019\u4e00\u985e\u578b\u7279\u5fb5\u4e2d\u7684\u591a \u5982Ozsoy\u7b49\u4eba\u65bc2010\u5e74\u63d0\u51fa\u4e8c\u7a2e\u57fa\u65bcLSA\u7684\u65b9\u6cd5\u4f86\u6539\u9032Steinberger\u8207Jezek\u7684\u65b9\u6cd5[10]\uff0c\u8655</td></tr><tr><td>\u7a2e\u7279\u5fb5\u4f86\u5206\u6790\u8a9e\u53e5\u91cd\u8981\u6027\u3002\u56e0\u6b64\u5728\u672c\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u63d0\u51fa\u4e00\u500b\u57fa\u65bc\u57fa\u56e0\u6f14\u7b97\u6cd5(Genetic \u7406\u571f\u8033\u5176\u6587\u591a\u6587\u4ef6\u6458\u8981[8]\u3002\u4ed6\u5011\u767c\u73fe\u6240\u63d0\u51fa\u7684\u65b9\u6cd5\u4e2dCross\u4f5c\u6cd5\u80fd\u5920\u5f97\u5230\u5f88\u597d\u7684\u6548\u679c\uff0c</td></tr><tr><td>Algorithms)\u7684\u7d44\u5408\u5f0f\u591a\u6587\u4ef6\u6458\u8981\u6a21\u578b\uff0c\u4ee5\u63d0\u5347\u591a\u6587\u4ef6\u6458\u8981\u7684\u6548\u80fd\u8868\u73fe\u3002\u5728\u904e\u5f80\u591a\u6587\u4ef6\u6458 \u5728\u5be6\u9a57\u4e2d\u6bd4\u5176\u4ed6LSA\u4f5c\u6cd5\u7684\u6458\u8981\u6a21\u578b\u90fd\u597d\u3002Hachey\u7b49\u4eba\u65bc2006\u5e74\u8a0e\u8ad6SVD\u5c0d\u591a\u6587\u4ef6\u6458\u8981</td></tr><tr><td>\u8981\u7814\u7a76\u4e2d\uff0cChali \u7b49\u4eba\u4ee5SVM (Support Vector Machines) \u4e5f\u66fe\u63d0\u51fa\u4e00\u500b\u7d44\u5408\u5f0f\u7684\u6458\u8981\u6a21 \u7684\u6548\u679c[5]\u3002\u4ed6\u5011\u767c\u73feSVD\u52a0\u4e0a\u5b57\u8a5e\u5171\u73fe\u7279\u5fb5\u5f8c\uff0c\u96d6\u7136\u6458\u8981\u6548\u679c\u8207TF-IDF\u7684\u4f5c\u6cd5\u76f8\u6bd4\u4e4b</td></tr><tr><td>\u578b[2]\u3002\u7136\u800c\u9019\u500bSVM\u7d44\u5408\u5f0f\u6458\u8981\u6a21\u578b\u5247\u662f\u91dd\u5c0d\u7279\u5b9a\u65b9\u5f0f\u7684\u4f7f\u7528\u8005\u9700\u6c42\u4f86\u9032\u884c\u591a\u6587\u4ef6\u6458 \u4e0b\u4e0d\u5206\u8ed2\u8f0a\uff0c\u4f46\u662f\u6bd4\u55ae\u7d14\u4f7f\u7528\u5b57\u8a5e\u5171\u73fe\u7279\u5fb5\u7684\u6548\u679c\u8981\u597d\u3002</td></tr><tr><td>\u8981\uff0c\u4e26\u4e0d\u662f\u901a\u7528\u578b\u591a\u6587\u4ef6\u6458\u8981(Generic Multi-document Summarization)\u3002\u672c\u7814\u7a76\u6240\u63d0\u51fa\u7684 Erkan\u8207Radev\u65bc2004\u5e74\u63d0\u51faDegree Centrality \u8207LexRank\u5169\u7a2e\u7db2\u8def\u6458\u8981\u6a21\u578b[3]\u3002\u4ed6</td></tr></table>", | |
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