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1 Vol. 3 No (June 2010) TSC2 Automatic Evaluation of Text Summaries by Using Paraphrase Kazuho Hirahara, 1 Hidetsugu Nanba, 1 Toshiyuki Takezawa 1 and Manabu Okumura 2 The evaluation of computer-produced summaries has been recognized as an important research problem for automatic text summarization. Traditionally, computer-produced summaries were evaluated automatically by n-gram overlap with human-produced texts. However, these methods cannot evaluate summaries correctly, if the n-grams do not overlap between computer-produced and human-produced summaries, even though the two summaries convey the same meaning. We explored the use of paraphrases for the refinement of traditional automatic methods for summary evaluation. To confirm the effectiveness of our method, we conducted some experiments using the data from the Text Summarization Challenge 2. We found that the use of paraphrases created using a statistical machine translation technique improved the traditional evaluation methods ) 4),5) 18) Graduate School of Information Sciences, Hiroshima City University 2 Precision and Intelligence Laboratory, Tokyo Institute of Technology 91 c 2010 Information Processing Society of Japan
2 BLEU ROUGE BLEU 14) BLEU 1 N 9) BLEU BLEU ROUGE 10) Lin ROUGE 1 ROUGE-N ROUGE-N N S {Reference Summaries} gram ROUGE-N = Count n S match(gram n ) Count(gram (1) S {Reference Summaries} gram n S n) n N gram n Count match (gram n ) N Lin N 1 4 N =1 2 N= ) 16) 1) 16) 2 carbon dioxide carbon dioxide 1 1) 8),11) 2 2 7),18) Zhou ParaEval 18) (1) (2) Greedy ROUGE Zhou ParaEval ROUGE Zhou Zhou ParaEval Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
3 93 Kauchak 7) Kauchak 3. TSC2 3) % A % B % C % D % E % A 3 B C D X Y X Y E A D Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
4 A D ParaEval ParaEval ParaEval ParaEval ParaEval (1) C D (2) (1) B A (3) (1) (2) (4) (1) (2) (3) (1) (2) Greedy (4) Lin 10) ParaEval ParaEval (1) (2) (1) C D (3) (1) (2) B A (4) (1) (2) (3) 4.2 ParaEval 4 SMT SMT DS WN WordNet NTT NTT SMT Zhou X Y X Y The Daily Yomiuri 150,000 17) GIZA ,858 3 DS 1) (1) CaboCha taku/software/cabocha/ Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
5 95 1 Table 1 Paraphrasing methods for text evaluation. Table 2 2 Correspondence of the classification of paraphrases and paraphrasing methods. SMT DS WordNet WN NTT NTT (2) (1) (3) (4) SMART 15) (2) (3) WordNet WN WordNet ), 1 WordNet synset WordNet synset NTT NTT NTT SMT DS Word NTT Net A 20.1% (64/318) B 24.5% (78/318) C 11.9% (38/318) D 12.3% (39/318) E 31.2% (99/318) TSC2 3) 1, % Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
6 Table 3 The distribution of evaluation results by three human subjects. Table 4 List of 15 proposed methods and a baseline method. A B C A D 4 2, 3 SMT DS NTT NTT WordNet WN 4 15 ROUGE EX A 7.55 B 7.29 C A B C D B D ROUGE Lin 10) ROUGE-1 ROUGE-2 TSC2 Nanba 12) ROUGE-1 ROUGE SMT DS WordNet NTT S D W N SD SW SN DW DN WN SDW SDN SWN DWN SDWN ROUGE-1 EX ParaEval EX-5 EX-8 ParaEval ParaEval EX-1 9 EX-2 9 EX-3 9 EX-4 9 ParaEval EX-5 9 EX-6 9 Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
7 97 EX-7 9 EX ParaEval ParaEval EX-1 ROUGE DW ROUGE % 6 15 DN ROUGE % 1 9 B Table 5 Table 6 5 ParaEval Evaluation results using an extract-type reference summary (ParaEval method). EX-1 EX-2 S SMT D DS W WordNet N NTT SD SW SN DW DN WN SDW SDN SWN DWN SDWN ROUGE ParaEval Evaluation results using an abstract-type reference summary (ParaEval method). EX-3 EX-4 S SMT D DS W WordNet N NTT SD SW SN DW DN WN SDW SDN SWN DWN SDWN ROUGE Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
8 98 7 ParaEval 8 ParaEval Table 7 Evaluation results using an extract-type reference summary (Reverse ParaEval method). Table 8 Evaluation results using an abstract-type reference summary (Reverse ParaEval method). EX-5 EX-6 EX-7 EX-8 S SMT D DS W WordNet N NTT SD SW SN DW DN WN SDW SDN SWN DWN SDWN S SMT D DS W WordNet N NTT SD SW SN DW DN WN SDW SDN SWN DWN SDWN ROUGE ROUGE ParaEval ParaEval D ROUGE % 8 SWN ROUGE % ParaEval 6. EX-1 ROUGE-1 EX-3 EX-1 EX-4 1 ParaEval ParaEval EX-1 EX-4 ParaEval EX-5 EX-8 ParaEval EX-2 EX ParaEval ParaEval ParaEval (1) ParaEval (2) Para- Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
9 99 Eval (2) ParaEval (3) 1 ParaEval ParaEval ParaEval EX-1 EX-4 ParaEval EX-5 EX-8 SDWN 1 ParaEval ParaEval ParaEval ParaEval ParaEval ParaEval SMT DS ParaEval 1 ParaEval EX-1 EX-5 EX-3 EX-7 EX-8 EX-4 1 Kauchak 7) 2 place Kauchak ParaEval 4 D W Word- Net S N NTT S N 30 Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
10 Zhou ParaEval Zhou WordNet NTT TSC ParaEval ParaEval 1) Vol.49, No.3, pp (2008). 2) Bond, F., Isahara, H., Uchimoto, K., Kuribayashi, T. and Kanzaki, K: Extending the Japanese WordNet 15 pp (2009). 3) Fukushima, T., Okumura, M. and Nanba, H: Text Summarization Challenge 2/Text Summarization Evaluation at NTCIR Workshop 3, Working Notes of the 3rd NTCIR Workshop Meeting, PART V, pp.1 7 (2002). 4) Vol.47, No.6, pp (2006). 5) Hovy, E., Lin, C.-Y., Zhou, L. and Fukumoto, J: Automated Summarization Evaluation with Basic Elements, Proc. 5th Conference on Language Resources and Evaluation (2006). 6) Vol.11, No.5, pp (2004) 7) Kauchak, D. and Barzilay, R: Paraphrasing for Automatic Evaluation, Proc Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pp (2006). 8) Lee, L: Measures of Distributional Similarity, Proc. 37th Annual Meeting of the Association for Computational Linguistics, pp (1999). 9) Lin, C.-Y. and Hovy, E: Automatic Evaluation of Summaries Using N-gram Cooccurrence Statistics, Proc. 4th Meeting of the North American Chapter of the Association for Computational Linguistics and Human Language Technology, pp (2003). 10) Lin, C.-Y.: ROUGE: A Package for Automatic Evaluation of Summaries, Proc. ACL-04 Workshop Text Summarization Branches Out, pp (2004). 11) Lin, D.: Automatic Retrieval and Clustering of Similar Words, Proc. 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics, pp (1998). 12) Nanba, H. and Okumura, M: An Automatic Method for Summary Evaluation Using Multiple Evaluation Results by a Manual Method, Proc. COLING/ACL 2006 Main Conference Poster Sessions, pp (2006). 13) Vol.23, No.1, pp (2008). 14) Papineni, K., Roukos, S., Ward, T. and Zhu, W.-J.: BLEU: A Method for Automatic Evaluation of Machine Translation, IBM Research Report, RC22176 (W ) (2001). 15) Salton, G.: The SMART Retrieval System. Experiments in Automatic Document Processing, Prentice-Hall, Inc., Upper Saddle River, NJ (1971). 16) 14 pp (2008). 17) Utiyama, M. and Isahara, H.: Reliable Measures for Aligning Japanese-English News Articles and Sentences, Proc. 41st Annual Meeting of the Association for Computational Linguistics, pp (2003). 18) Zhou, L., Lin, C.-Y., Munteanu, D.S. and Hovy, E.: ParaEval: Using Paraphrases to Evaluate Summaries Automatically, Proc Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pp (2006). ( ) ( ) Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
11 ISS ACL ACM AAAI ACL Vol. 3 No (June 2010) c 2010 Information Processing Society of Japan
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The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website by the author(s) under the agreement with the IPSJ.
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2018 (412837) 4.1 % 7.5 % Abstract Recently, various methods for improving computial performance have been proposed. One of these various methods is Multi-core. Multi-core can execute processes in parallel