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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This paper provides a progress report on ICSI s Meeting Project, including both the data collected and annotated as part of the pro-ject, as well as the research lines such materials support. We include a general description of the official ICSI Meeting Corpus , as currently available through the Linguistic Data Consortium, discuss some of the existing and planned annotations which augment the basic transcripts provided there, and describe several research efforts that make use of these materials. The corpus supports wide-ranging efforts, from low-level processing of the audio signal (including automatic speech transcription, speaker tracking, and work on far-field acoustics) to higher-level analyses of meeting structure, content, and interactions (such as topic and sentence segmentation, and automatic detection of dialogue acts and meeting hot spots ). 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). 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. In this paper, we present a novel supervised approach to the problem of summarizing email conversations and modeling dialogue acts. We assume that there is a relationship between dialogue acts and important sentences. Based on this assumption, we introduce a sequential graphical model approach which simultaneously summarizes email conversation and models dialogue acts. We compare our model with sequential and non-sequential models, which independently conduct the tasks of extractive summarization and dialogue act modeling. An empirical evaluation shows that our approach significantly outperforms all baselines in classifying correct summary sentences without losing performance on dialogue act modeling task. This paper describes a summarization system for technical chats and emails on the Linux kernel. To reflect the complexity and sophistication of the discussions, they are clustered according to subtopic structure on the sub-message level, and immediate responding pairs are identified through machine learning methods. A resulting summary consists of one or more mini-summaries, each on a subtopic from the discussion. We have explored the usefulness of incorporating speech and discourse features in an automatic speech summarization system applied to meeting recordings from the ICSI Meetings corpus. By analyzing speaker activity, turn-taking and discourse cues, we hypothesize that such a system can outperform solely text-based methods inherited from the field of text summarization. The summarization methods are described, two evaluation methods are applied and compared, and the results clearly show that utilizing such features is advantageous and efficient. Even simple methods relying on discourse cues and speaker activity can outperform text summarization approaches. 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. This paper presents a pilot study of opinion summarization on conversations. We create a corpus containing extractive and abstractive summaries of speaker's opinion towards a given topic using 88 telephone conversations. We adopt two methods to perform extractive summarization. The first one is a sentence-ranking method that linearly combines 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 additional information about sentence-to-sentence relations extracted based on dialogue structure. Our evaluation results show that both methods significantly outperform the baseline approach that extracts the longest utterances. In particular, we find that incorporating dialogue structure in the graph-based method contributes to the improved system performance.