Minh-Tien Nguyen
Tran Viet Cuong
ABSTRACT
Social media presents a new method for readers who can freely discuss the content of an event mentioned in a Web document by posting relevant comments. The comments provide additional information which can be used to enrich the information of the main document. This paper introduces a new model which integrates user comments into the summarization process. While prior methods consider the same topic number between sentences and comments of a document, we argue that sentences and comments should own their different topics and they also share common hidden topics in term of same or inferred words. From this, we define a new objective function which jointly combines sentences and comments to achieve global optimization. The objective function is optimized by our non-negative matrix factorization algorithm to find out weights of sentence-matrix and comment-matrix for ranking sentences and comments. Experimental results on two datasets in English and Vietnamese show that our model achieves promising results for single-document summarization.
- Kathleen McKeown Ani Nenkova. 2011. Automatic Summarization. Foundations and Trends in Information Retrieval, 5(2-3): 103--233 (2011).Google Scholar
Cross Ref
- Siddhartha Banerjee, Prasenjit Mitra, and Kazunari Sugiyama. 2015. Multi-Document Abstractive Summarization Using ILP Based Multi-Sentence Compression. In Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 1208--1214.Google ScholarDigital Library
- Ziqiang Cao, Furu Wei, Sujian Li, Wenjie Li, Ming Zhou, and Houfeng Wang. 2015. Learning Summary Prior Representation for Extractive Summarization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics ACL (2), pp. 829--833. Association for Computational Linguistics.Google ScholarCross Ref
- Gunes Erkan and Dragomir R. Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research, 22, pp. 457--479 (2004).Google ScholarDigital Library
- Wei Gao, Peng Li, and Kareem Darwish. 2012. Joint Topic Modeling for Event Summarization across News and Social Media Streams. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1173--1182. ACM.Google ScholarDigital Library
- Yihong Gong and Xin Liu. 2001. Generic Text Summarization using Relevant Measure and Latent Semantic Analysis. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 19--25. ACM.Google ScholarDigital Library
- Masahiro Kohjima, Tatsushi Matsubayashi, and Hiroshi Sawada. 2015. Probabilistic Non-negative Inconsistent-resolution Matrices Factorization. In 24th ACM International on Conference on Information and Knowledge Management (CIKM), pp. 1855--1858.Google Scholar
- Daniel D. Lee and H. Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, no. 6755, pp. 788--791 (1999).Google ScholarCross Ref
- Daniel D. Lee and H. Sebastian Seung. 2001. Algorithms for non-negative matrix factorization, In Advances in Neural Information Processing Systems, pp. 556--562. Advances in Neural Information Processing Systems 13 (2001).Google Scholar
- Ju-Hong Lee, Sun Park, Chan-Min Ahn, and Daeho Kim. 2009. Automatic generic document summarization based on non-negative matrix factorization. Information Processing & Management 45(1), pp. 20--34 (2009).Google ScholarDigital Library
- Chen Li, Zhongyu Wei, Yang Liu, Yang Jin, and Fei Huang. 2016. Using Relevant Public Posts to Enhance News Article Summarization. In COLING, pp. 557--566.Google Scholar
- Chih-Jen Lin. 2007. Projected gradient methods for nonnegative matrix factorization. Neural computation, 19(10), pp. 2756--2779 (2007).Google Scholar
- Chin-Yew Lin and Eduard H. Hovy. 2003. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, pp. 71--78. Association for Computational Linguistics.Google Scholar
- Preslav Nakov, Antonia Popova, and Plamen Mateev. 2001. Weight functions impact on LSA performance. In EuroConference RANLP, pp. 187--193.Google Scholar
- Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 3075--3081.Google ScholarDigital Library
- Ani Nenkova. 2005. Automatic text summarization of newswire: lessons learned from the document understanding conference. In AAAI, vol. 5, pp. 1436--1441.Google ScholarDigital Library
- Minh-Tien Nguyen, Tran Viet Cuong, Nguyen Xuan Hoai, and Minh-Le Nguyen. 2017. Utilizing User Posts to Enrich Web Document Summarization with Matrix Co-factorization. In Proceedings of the Eighth International Symposium on Information and Communication Technology (SoICT), pp. 70--77. ACM.Google ScholarDigital Library
- Minh-Tien Nguyen, Viet Dac Lai, Phong-Khac Do, Duc-Vu Tran, and Minh-Le Nguyen. 2016. VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization. In The 12th Workshop on Asian Language Resources, pp. 38--48. Association for Computational Linguistics.Google Scholar
- Minh-Tien Nguyen and Minh-Le Nguyen. 2016. SoRTESum: A Social Context Framework for Single-Document Summarization. In European Conference on Information Retrieval, pp. 3--14. Springer International Publishing.Google Scholar
- Minh-Tien Nguyen, Chien-Xuan Tran, Duc-Vu Tran, and Minh-Le Nguyen. 2016. SoLSCSum: A Linked Sentence-Comment Dataset for Social Context Summarization. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2409--2412. ACM.Google ScholarDigital Library
- Minh-Tien Nguyen, Duc-Vu Tran, and Minh-Le Nguyen. 2018. Social Context Summarization using User-generated Content and Third-party Sources. Knowledge-Based Systems, 144(2018), pp. 51--64. Elsevier (2018).Google ScholarCross Ref
- Minh-Tien Nguyen, Viet Cuong Tran, Xuan Hoai Nguyen, and Le-Minh Nguyen. 2019. Web Document Summarization by Exploiting Social Context with Matrix Co-factorization. Information Processing & Management, 56(3), pp. 495--515 (2019).Google ScholarDigital Library
- Sun Park, Ju-Hong Lee, Chan-Min Ahn, Jun Sik Hong, and Seok-Ju Chun. 2006. Query Based Summarization Using Non-negative Matrix Factorization. In Knowledge-Based Intelligent Information and Engineering Systems, pp. 84--89. Springer Berlin/Heidelberg.Google Scholar
- Dou Shen, Jian-Tao Sun, Hua Li, Qiang Yang, and Zheng Chen. 2007. Document Summarization Using Conditional Random Fields. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), vol. 7, pp. 2862--2867.Google ScholarDigital Library
- Doina Tatar, Emma Tamaianu-Morita, Andreea Mihis, and Dana Lupsa. 2008. Summarization by logic segmentation and text entailment. In Advances in Natural Language Processing and Applications (CICLING), pp. 15--26.Google Scholar
- Dingding Wang, Tao Li, Shenghuo Zhu, and Chris Ding. 2008. Multidocument summarization via sentence-level semantic analysis and symmetric matrix factorization. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 307--314. ACM.Google ScholarDigital Library
- Zhongyu Wei and Wei Gao. 2014. Utilizing Microblogs for Automatic News Highlights Extraction. In COLING, pp. 872--883.Google Scholar
- Zhongyu Wei and Wei Gao. 2015. Gibberish, Assistant, or Master?: Using Tweets Linking to News for Extractive Single-Document Summarization. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1003--1006. ACM.Google ScholarDigital Library
- Kristian Woodsend and Mirella Lapata. 2012. Multiple Aspect Summarization Using Integer Linear Programming. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 233--243. Association for Computational Linguistics.Google ScholarDigital Library
- Zi Yang, Keke Cai, Jie Tang, Li Zhang, Zhong Su, and Juanzi Li. 2011. Social Context Summarization. In Proceedings of the 34th International SIGIR Conference on Research and Development in Information Retrieval, pp. 255--264. ACM.Google ScholarDigital Library
Index Terms
- Exploiting User Comments for Document Summarization with Matrix Factorization
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