Abstract
The TAC 2010 summarization track initiated a new task—aspect-guided summarization—that centers on textual aspects embodied as particular kinds of information of a text. We observe that aspect-guided summaries not only address highly specific user need, but also facilitate content-level coherence by using aspect information. In this article, we present a full-fledged approach to aspect-guided summarization with a focus on summary coherence. Our summarization approach depends on two prerequisite subtasks: recognizing aspect-bearing sentences in order to do sentence extraction, and modeling aspect-based coherence with an HMM model in order to predict a coherent sentence ordering. Using the manually annotated TAC 2010 and 2010 datasets, we validated the effectiveness of our proposed methods for those subtasks. Drawing on the empirical results, we proceed to develop an aspect-guided summarizer based on a simple but robust base summarizer. With sentence selection guided by aspect information, our system is one of the best on TAC 2011. With sentence ordering predicted by the aspect-based HMM model, the summaries achieve good coherence.
- Barzilay, R. and Lapata, M. 2008. Modeling local coherence: An entity-based approach. Comput. Linguist. 34, 1--34. Google ScholarDigital Library
- Barzilay, R., and Lee, L. 2004. Catching the drift: Probabilistic content models, with applications to generation and summarization. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL). 113--120.Google Scholar
- Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 4--5, 993--1022. Google ScholarDigital Library
- Boutell, M. R., Luo, J., Shen, X., and Brown, C. M. 2004. Learning multi-label scene classification. Pattern Recogn. 37, 9, 1757--71.Google ScholarCross Ref
- Daumé III, H. and Marcu, D. 2006. Bayesian query-focused summarization. In Proceedings of the Meeting of the Association of Computational Linguistics (ACL). 305--312. Google ScholarDigital Library
- Elsner, M., Austerweil, J. and Charniak, E. 2007. A unified local and global model for discourse coherence. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL). 436--443.Google Scholar
- Fuentes, M., Alfonseca, E., and Rodríguez, H. 2007. Support vector machines for query-focused summarization trained and evaluated on pyramid data. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (Companion Volume Proceedings of the Demo and Poster Sessions). 57--60. Google ScholarDigital Library
- Genest, P. and Lapalme, G. 2010. Text generation for abstractive summarization. In Proceedings of the 3rd Text Analysis Conference. National Institute of Standards and Technology.Google Scholar
- Ji, H., Favre, B., Lin, W., Gillick, D., Hakkani-Tur, D., and Grishman, R. 2011. Open-Domain multi-document summarization via information extraction: Challenges and prospects. In Multi-Source, Multilingual Information Extraction and Summarisation Volume of Theory and Applications of Natural Language Processing. Springer.Google Scholar
- Joachims, T. 1999. Transductive inference for text classification using support vector machines. In Proceedings of the 16th International Conference on Machine Learning (ICML'99). Google ScholarDigital Library
- Klein, D., and Manning, C. D. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st Meeting of the Association for Computational Linguistics. 423--430. Google ScholarDigital Library
- Lapata, M. 2003. Probabilistic text structuring: Experiments with sentence ordering. In Proceedings of the Annual Meeting of the Association of Computational Linguistics (ACL). 545--552. Google ScholarDigital Library
- Lapata, M. 2006. Automatic evaluation of information ordering: Kendall's tau. Comput. Linguist. 32, 4, 1--14. Google ScholarDigital Library
- Li, P., Wang, Y., Gao, W., and Jiang, J. 2011. Generating aspect-oriented multi-document summarization with event-aspect model. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1137--1146. Google ScholarDigital Library
- Li, W., Li, W., and Lu, Q. 2006. Mining implicit entities in queries. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC'06). 24--26.Google Scholar
- Lin, C.-Y. and Hovy, E. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the Human Technology Conference (HLT-NAACL'03). 71--78. Google ScholarDigital Library
- Mani, I. and Bloedorn E. 1999. Summarizing similarities and differences among related documents. Inf. Retr. 1, 35--67. Google ScholarDigital Library
- Mcknight, L. and Srinivasan, P. 2003. Categorization of sentence types in medical abstracts. In Proceedings of the American Medical Informatics Association Annual Symposium. 440--444.Google Scholar
- Owczarzak, K. and Dang, H. T. 2011. Who wrote what where: Analyzing the content of human and automatic summaries. In Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages. 25--32. Google ScholarDigital Library
- Patwardhan, S. 2010. Widening the field of view of information extraction through sentential event recognition. Ph.D. dissertation, The University of Utah. Google ScholarDigital Library
- Rand, W. M. 1971. Objective criteria for the evaluation of clustering methods. J. Amer. Statist. Assoc. 66, 336, 846--850.Google ScholarCross Ref
- Riloff, E. 1996. Automatically generating extraction patterns from untagged text. In Proceedings of the 13th National Conference on Artificial Intelligence. 1044--1049. Google ScholarDigital Library
- Schilder, F. and Kondadadi, R. 2008. FastSum: Fast and accurate query-based multi-document summarization. In Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL'08: HLT). 205--208. Google ScholarDigital Library
- Stevenson, M. and Greenwood, M. A. 2005. A semantic approach to ie pattern recognition. In Proceedings of the 43rd Annual Meeting of the Association of Computational Linguistics (ACL). 379--386. Google ScholarDigital Library
- Teufel, S. and Moens, M. 1999. Argumentative classification of extracted sentences as a first step towards flexible abstracting. In Advances in Automatic Text Summarization, I. Mani and M. T. Maybury, Eds., MIT Press, Cambridge, MA, 155--171.Google Scholar
- Teufel, S. and Moens, M. 2002. Summarizing scientific articles: Experiments with relevance and rhetorical status. Comput. Linguist. 28, 4, 409--445. Google ScholarDigital Library
- Tsoumakas, G. and Katakis, I. 2007. Multi label classification: An overview. Int. J. Data Warehous. Min. 3, 3, 1--13.Google ScholarCross Ref
- Vanderwende, L., Suzuki, H., Brockett, C., and Nenkova, A. 2007. Beyond sumbasic: Task-Focused summarization with sentence simplification and lexical expansion. Inf. Process. Manag. 43, 6, 1606--1618. Google ScholarDigital Library
- Vapnik, V. 1998. Statistical Learning Theory. John Wiley & Sons, New York.Google Scholar
- Wan, X., Yang, J., and Xiao, J. 2007. Towards a unified approach based on affinity graph to various multi-document summarizations. In Proceedings of the 11th European Conference. 297--308. Google ScholarDigital Library
- Wang, L., Shen, X., and Pan, W. 2007. On transductive support vector machines. In Prediction and Discovery, J. Verducci, X. Shen, and J. Lafferty, Eds., American Mathematical Society.Google Scholar
- Yangarber, R. 2003. Counter-Training in the discovery of semantic patterns. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL'03). 343--350. Google ScholarDigital Library
- Zhang, R., Ouyang, Y., and Li, W. 2011. Guided summarization with aspect recognition. In Proceedings of Textual Analysis Conference (TAC'11).Google Scholar
- Zhou, L., Ticrea, M., and Hovy, E. 2004. Multidocument biography summarization. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP'04). 434--441.Google Scholar
Index Terms
- Towards content-level coherence with aspect-guided summarization
Recommendations
Towards coherent single-document summarization: an integer linear programming-based approach
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingAutomatic Text Summarization (ATS) is a viable option to reduce the content of textual documents, e.g., as a possible preprocessing step in many text mining applications. Single-document extractive summarizers have been developed based on different ...
Applying two-level reinforcement ranking in query-oriented multidocument summarization
Sentence ranking is the issue of most concern in document summarization today. While traditional feature-based approaches evaluate sentence significance and rank the sentences relying on the features that are particularly designed to characterize the ...
Intertopic information mining for query-based summarization
In this article, the authors address the problem of sentence ranking in summarization. Although most existing summarization approaches are concerned with the information embodied in a particular topic (including a set of documents and an associated ...
Comments