Automatic Summarization Considering Thread Structure and Time Series in Electronic Bulletin Board System for Discussion

Abstract

On electronic bulletin board systems for discussion, a topic the users argue diversifies into multiple subtopics, and the entire structure becomes complicated. It is helpful to show users summarizations of the arguments because they can help in understanding the contents more easily without looking over from beginning to end of the discussion forum. The purpose of this paper is to propose an automatic summarization method of a single thread considering time series, reply relationships and user information. In the proposed method, a thread is restructured in several clusters by hierarchical clustering, and important sentences compressed with linguistic relationship of predicate argument structures are selected within each cluster using LexRank, which is a stochastic graph-based method for computing the relative importance of textual units. Finally, we conducted quantitative and qualitative analysis, comparing the proposed method with MMR. Both experimental results demonstrate that the proposed method can reduce redundancies more and extract fewer sentences unrelated to the whole context of the summary than the baseline. However, the proposed method included fewer important words than the baseline.