Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 552–559, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction Xiaojun Wan Jianwu Yang Jianguo Xiao Institute of Computer Science and Technology Peking University, Beijing 100871, China {wanxiaojun,yangjianwu,xiaojianguo}@icst.pku.edu.cn Abstract Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated inde- pendently. This paper proposes a novel it- erative reinforcement approach to simulta- neously extracting summary and keywords from single document under the assump- tion that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and key- words by fusing three kinds of relation- ships between sentences and words, either homogeneous or heterogeneous. Experi- mental results show the effectiveness of the proposed approach for both tasks. The cor- pus-based approach is validated to work almost as well as the knowledge-based ap- proach for computing word semantics. 1 Introduction Text summarization is the process of creating a compressed version of a given document that de- livers the main topic of the document. Keyword extraction is the process of extracting a few salient words (or phrases) from a given text and using the words to represent the text. The two tasks are simi- lar in essence because they both aim to extract concise representations for documents. Automatic text summarization and keyword extraction have drawn much attention for a long time because they both are very important for many text applications, including document retrieval, document clustering, etc. For example, keywords of a document can be used for document indexing and thus benefit to improve the performance of document retrieval, and document summary can help to facilitate users to browse the search results and improve users’ search experience. Text summaries and keywords can be either query-relevant or generic. Generic summary and keyword should reflect the main topics of the document without any additional clues and prior knowledge. In this paper, we focus on generic document summarization and keyword extraction for single documents. Document summarization and keyword extrac- tion have been widely explored in the natural lan- guage processing and information retrieval com- munities. A series of workshops and conferences on automatic text summarization (e.g. SUMMAC, DUC and NTCIR) have advanced the technology and produced a couple of experimental online sys- tems. In recent years, graph-based ranking algo- rithms have been successfully used for document summarization (Mihalcea and Tarau, 2004, 2005; ErKan and Radev, 2004) and keyword extraction (Mihalcea and Tarau, 2004). Such algorithms make use of “voting” or “recommendations” between sentences (or words) to extract sentences (or key- words). Though the two tasks essentially share much in common, most algorithms have been de- veloped particularly for either document summari- zation or keyword extraction. Zha (2002) proposes a method for simultaneous keyphrase extraction and text summarization by using only the heterogeneous sentence-to-word relationships. Inspired by …

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