Multi-Document Summarization of Evaluative Text

Multi-Document Summarization of Evaluative Text Giuseppe Carenini, Raymond Ng, and Adam Pauls Deptartment of Computer Science University of British Columbia Vancouver, Canada � carenini,rng,adpauls � @cs.ubc.ca Abstract We present and compare two approaches to the task of summarizing evaluative ar- guments. The first is a sentence extraction- based approach while the second is a lan- guage generation-based approach. We evaluate these approaches in a user study and find that they quantitatively perform equally well. Qualitatively, however, we find that they perform well for different but complementary reasons. We conclude that an effective method for summarizing eval- uative arguments must effectively synthe- size the two approaches. 1 Introduction Many organizations are faced with the challenge of summarizing large corpora of text data. One im- portant application is evaluative text, i.e. any doc- ument expressing an evaluation of an entity as ei- ther positive or negative. For example, many web- sites collect large quantities of online customer re- views of consumer electronics. Summaries of this literature could be of great strategic value to prod- uct designers, planners and manufacturers. There are other equally important commercial applica- tions, such as the summarization of travel logs, and non-commercial applications, such as the summa- rization of candidate reviews. The general problem we consider in this paper is how to effectively summarize a large corpora of evaluative text about a single entity (e.g., a prod- uct). In contrast, most previous work on multi- document summarization has focused on factual text (e.g., news (McKeown et al., 2002), biogra- phies (Zhou et al., 2004)). For factual documents, the goal of a summarizer is to select the most im- portant facts and present them in a sensible or- dering while avoiding repetition. Previous work has shown that this can be effectively achieved by carefully extracting and ordering the most infor- mative sentences from the original documents in a domain-independent way. Notice however that when the source documents are assumed to con- tain inconsistent information (e.g., conflicting re- ports of a natural disaster (White et al., 2002)), a different approach is needed. The summarizer needs first to extract the information from the doc- uments, then process such information to identify overlaps and inconsistencies between the different sources and finally produce a summary that points out and explain those inconsistencies. A corpus of evaluative text typically contains a large number of possibly inconsistent ‘facts’ (i.e. opinions), as opinions on the same entity feature may be uniform or varied. Thus, summarizing a corpus of evaluative text is much more similar to summarizing conflicting reports than a consistent set of factual documents. When there are diverse opinions on the same issue, the different perspec- tives need to be included in the summary. Based on this observation, we argue that any strategy to effectively summarize evaluative text about a single entity should rely on a preliminary phase of information extraction from the target corpus. In particular, the summarizer should at least know for each document: what features of the entity were evaluated, the polarity …

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