A Pilot Study of Opinion Summarization in Conversations

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 331–339, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics A Pilot Study of Opinion Summarization in Conversations Dong Wang Yang Liu The University of Texas at Dallas dongwang,yangl@hlt.utdallas.edu Abstract This paper presents a pilot study of opinion summarization on conversations. We create a corpus containing extractive and abstrac- tive summaries of speaker’s opinion towards a given topic using 88 telephone conversa- tions. We adopt two methods to perform ex- tractive summarization. The first one is a sentence-ranking method that linearly com- bines scores measured from different aspects including topic relevance, subjectivity, and sentence importance. The second one is a graph-based method, which incorporates topic and sentiment information, as well as addi- tional information about sentence-to-sentence relations extracted based on dialogue struc- ture. Our evaluation results show that both methods significantly outperform the baseline approach that extracts the longest utterances. In particular, we find that incorporating di- alogue structure in the graph-based method contributes to the improved system perfor- mance. 1 Introduction Both sentiment analysis (opinion recognition) and summarization have been well studied in recent years in the natural language processing (NLP) com- munity. Most of the previous work on sentiment analysis has been conducted on reviews. Summa- rization has been applied to different genres, such as news articles, scientific articles, and speech do- mains including broadcast news, meetings, conver- sations and lectures. However, opinion summariza- tion has not been explored much. This can be use- ful for many domains, especially for processing the increasing amount of conversation recordings (tele- phone conversations, customer service, round-table discussions or interviews in broadcast programs) where we often need to find a person’s opinion or attitude, for example, “how does the speaker think about capital punishment and why?”. This kind of questions can be treated as a topic-oriented opin- ion summarization task. Opinion summarization was run as a pilot task in Text Analysis Conference (TAC) in 2008. The task was to produce summaries of opinions on specified targets from a set of blog documents. In this study, we investigate this prob- lem using spontaneous conversations. The problem is defined as, given a conversation and a topic, a summarization system needs to generate a summary of the speaker’s opinion towards the topic. This task is built upon opinion recognition and topic or query based summarization. However, this problem is challenging in that: (a) Summarization in spontaneous speech is more difficult than well struc- tured text (Mckeown et al., 2005), because speech is always less organized and has recognition errors when using speech recognition output; (b) Senti- ment analysis in dialogues is also much harder be- cause of the genre difference compared to other do- mains like product reviews or news resources, as re- ported in (Raaijmakers et al., 2008); (c) In conversa- tional speech, information density is low and there are often off topic discussions, therefore presenting a need to identify utterances that are relevant to the topic. In this paper …

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