Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.
Dialog Summarization. To the best of our knowledge, none of the previous approaches have focused on debate dialog summarization. Prior research on spoken dialog summarization has explored lexical features, and information specific to meetings such as action items, speaker status, and structural discourse features. @cite_8 @cite_46 @cite_47 @cite_18 @cite_22 . In contrast to information content, examine how social phenomena such as politeness level affect summarization. Emotional information has also been observed in summaries of professional chats discussing technology @cite_5 . Other approaches use semantic similarity metrics to identify the most central or important utterances of a spoken dialog using Switchboard corpus @cite_32 . Dialog structure and prosodic features have been studied for finding patterns of importance and opinion summarization on Switchboard conversations @cite_25 @cite_15 . Additional parallel work is on summarizing email thread conversations using conversational features and dialog acts specific to the email domain @cite_16 @cite_34 .
@cite_47 @cite_18 @cite_22 @cite_8 @cite_15 @cite_32 @cite_34 @cite_5 @cite_46 @cite_16 @cite_25
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We present a novel approach to spoken dialogue summarization. Our system employs a set of semantic similarity metrics using the noun portion of WordNet as a knowledge source. So far, the noun senses have been disambiguated manually. The algorithm aims to extract utterances carrying the essential content of dialogues. We evaluate the system on 20 Switchboard dialogues. The results show that our system outperforms LEAD, RANDOM and TF*IDF baselines. Automatic summarization of open domain spoken dialogues is a new research area. This paper introduces the task, the challenges involved, and presents an approach to obtain automatic extract summaries for multi-party dialogues of four different genres, without any restriction on domain. We address the following issues which are intrinsic to spoken dialogue summarization and typically can be ignored when summarizing written text such as newswire data: (i) detection and removal of speech disfluencies; (ii) detection and insertion of sentence boundaries; (iii) detection and linking of cross-speaker information units (question-answer pairs). A global system evaluation using a corpus of 23 relevance annotated dialogues containing 80 topical segments shows that for the two more informal genres, our summarization system using dialogue specific components significantly outperforms a baseline using TFIDF term weighting with maximum marginal relevance ranking (MMR). In this paper we describe research on summarizing conversations in the meetings and emails domains. We introduce a conversation summarization system that works in multiple domains utilizing general conversational features, and compare our results with domain-dependent systems for meeting and email data. We find that by treating meetings and emails as conversations with general conversational features in common, we can achieve competitive results with state-of-the-art systems that rely on more domain-specific features. In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review analysis and propose a three-level framework for a recommendation dialogue system. In previous work we have explored a linguistic parsing approach to phrase extraction from reviews. In this paper we will describe an approach using statistical models such as decision trees and SVMs to select the most representative phrases from the extracted phrase set. We will also explain how to generate informative yet concise review summaries for dialogue purposes. Experimental results in the restaurant domain show that the proposed approach using decision tree algorithms achieves an outperformance of 13 compared to SVM models and an improvement of 36 over a heuristic rule baseline. Experiments also show that the decision-tree-based phrase selection model can achieve rather reliable predictions on the phrase label, comparable to human judgment. The proposed statistical approach is based on domain-independent learning features and can be extended to other domains effectively.