A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 385–392, Sydney, July 2006. c⃝2006 Association for Computational Linguistics A Bottom-up Approach to Sentence Ordering for Multi-document Summarization Danushka Bollegala Naoaki Okazaki ∗ Graduate School of Information Science and Technology The University of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan {danushka,okazaki}@mi.ci.i.u-tokyo.ac.jp ishizuka@i.u-tokyo.ac.jp Mitsuru Ishizuka Abstract Ordering information is a difficult but important task for applications generat- ing natural-language text. We present a bottom-up approach to arranging sen- tences extracted for multi-document sum- marization. To capture the association and order of two textual segments (eg, sen- tences), we define four criteria, chronol- ogy, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion until we obtain the overall segment with all sentences arranged. Our experimental results show a significant im- provement over existing sentence ordering strategies. 1 Introduction Multi-document summarization (MDS) (Radev and McKeown, 1999) tackles the information overload problem by providing a condensed ver- sion of a set of documents. Among a number of sub-tasks involved in MDS, eg, sentence ex- traction, topic detection, sentence ordering, infor- mation extraction, sentence generation, etc., most MDS systems have been based on an extraction method, which identifies important textual seg- ments (eg, sentences or paragraphs) in source doc- uments. It is important for such MDS systems to determine a coherent arrangement of the tex- tual segments extracted from multi-documents in order to reconstruct the text structure for summa- rization. Ordering information is also essential for ∗Research Fellow of the Japan Society for the Promotion of Science (JSPS) other text-generation applications such as Ques- tion Answering. A summary with improperly ordered sen- tences confuses the reader and degrades the qual- ity/reliability of the summary itself. Barzi- lay (2002) has provided empirical evidence that proper order of extracted sentences improves their readability significantly. However, ordering a set of sentences into a coherent text is a non- trivial task. For example, identifying rhetorical relations (Mann and Thompson, 1988) in an or- dered text has been a difficult task for computers, whereas our task is even more complicated: to reconstruct such relations from unordered sets of sentences. Source documents for a summary may have been written by different authors, by different writing styles, on different dates, and based on dif- ferent background knowledge. We cannot expect that a set of extracted sentences from such diverse documents will be coherent on their own. Several strategies to determine sentence order- ing have been proposed as described in section 2. However, the appropriate way to combine these strategies to achieve more coherent summaries re- mains unsolved. In this paper, we propose four criteria to capture the association of sentences in the context of multi-document summarization for newspaper articles. These criteria are integrated into one criterion by a supervised learning ap- proach. We also propose a bottom-up approach in arranging sentences, which repeatedly concate- nates …

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