PNR2: Ranking Sentences with Positive and Negative Reinforcement for Query-Oriented Update Summarization

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 489–496 Manchester, August 2008 PNR2: Ranking Sentences with Positive and Negative Reinforcement for Query-Oriented Update Summarization Abstract Query-oriented update summarization is an emerging summarization task very recently. It brings new challenges to the sentence ranking algorithms that require not only to locate the important and query-relevant information, but also to capture the new information when document collections evolve. In this paper, we propose a novel graph based sentence ranking algorithm, namely PNR2, for update summarization. Inspired by the intuition that “a sentence receives a positive influence from the sentences that correlate to it in the same collection, whereas a sentence receives a negative influence from the sentences that correlates to it in the different (perhaps previously read) collection”, PNR2 models both the positive and the negative mutual reinforcement in the ranking process. Automatic evaluation on the DUC 2007 data set pilot task demonstrates the effectiveness of the algorithm. 1 Introduction The explosion of the WWW has brought with it a vast board of information. It has become virtually impossible for anyone to read and understand large numbers of individual documents that are abundantly available. Automatic document summarization provides an effective means to © 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported license (http://creativecommons.org/licenses/by-nc- sa/3.0/). Some rights reserved. manage such an exponentially increased collection of information and to information seeking and condensing goals. support The main evaluation forum that provides benchmarks for researchers working on document summarization to exchange their ideas and experiences is the Document Understanding Conferences (DUC). The goals of the DUC evaluations are to enable researchers to participate in large-scale experiments upon the standard benchmark and to increase the availability of appropriate evaluation techniques. Over the past years, the DUC evaluations have evolved gradually from single-document summarization to multi-document summarization and from generic summarization to query- oriented summarization. Query-oriented multi- document summarization initiated in 2005 aims to produce a short and concise summary for a collection of topic relevant documents according to a given query that describes a user’s particular interests. Previous summarization tasks are all targeted on a single document or a static collection of documents on a given topic. However, the document collections can change (actually grow) dynamically when the topic evolves over time. New documents are continuously added into the topic during the whole lifecycle of the topic and normally they bring the new information into the topic. To cater for the need of summarizing a dynamic collection of documents, the DUC evaluations piloted update summarization in 2007. The task of update summarization differs from previous summarization tasks in that the latter aims to dig out the salient information in a topic while the former cares the information not only salient but also novel. Up to the present, the predominant approaches in document summarization regardless of the nature and the goals of the tasks have still been built upon the sentence extraction framework. Li Wenjie1, Wei Furu1,2, Lu Qin1, He Yanxiang2 …

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