Several approaches to automatic speech summarization are discussed below, using the ICSI Meetings corpus. We contrast feature-based approaches using prosodic and lexical features with maximal marginal relevance and latent semantic analysis approaches to summarization. While the latter two techniques are borrowed directly from the field of text summarization, feature-based approaches using prosodic information are able to utilize characteristics unique to speech data. We also investigate how the summarization results might deteriorate when carried out on ASR output as opposed to manual transcripts. All of the summaries are of an extractive variety, and are compared using the software ROUGE.

We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches for ranking all utterances in a sequence using CRFs. Our best performing system achieves 91.3 % of human performance when evaluated with the Pyramid evaluation metric, which represents a 3.9 % absolute increase compared to our most competitive non-sequential classifier. 1

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. 1.

Spontaneous conversation is optimized for human-human communication, but differs in some important ways from the types of speech for which human language technology is often developed. This overview describes four fundamental properties of spontaneousspeech that present challenges for spoken language applications because they violate assumptions often applied in automatic processing technology. 1.

Abstract. In this work we describe a large-scale extrinsic evaluation of automatic speech summarization technologies for meeting speech. The particular task is a decision audit, wherein a user must satisfy a complex information need, navigating several meetings in order to gain an understanding of how and why a given decision was made. We compare the usefulness of extractive and abstractive technologies in satisfying this information need, and assess the impact of automatic speech recognition (ASR) errors on user performance. We employ several evaluation methods for participant performance, including post-questionnaire data, human subjective and objective judgments, and an analysis of participant browsing behaviour. 1

Speech summarization technology, which extracts important information and removes irrelevant information from speech, is expected to play an important role in building speech archives and improving the efficiency of spoken document retrieval. However, speech summarization has a number of significant challenges that distinguish it from general text summarization. Fundamental problems with speech summarization include speech recognition errors, disfluencies, and difficulties of sentence segmentation. Typical speech summarization systems consist of speech recognition, sentence segmentation, sentence extraction, and sentence compaction components. Most research up to now has focused on sentence extraction, using LSA (Latent Semantic Analysis), MMR (Maximal Marginal Relevance), or feature-based approaches, among which no decisive method has yet been found. Proper sentence segmentation is also essential to achieve good summarization performance. How to objectively evaluate speech summarization results is also an important issue. Several measures, including families of SumACCY and ROUGE measures, have been proposed, and correlation analyses between subjective and objective evaluation scores have been performed. Although these measures are useful for ranking various summarization methods, they do not correlate well with human evaluations, especially when spontaneous speech is targeted. 1.

This article describes approaches to content-based access to spoken audio with a qualitative and tutorial emphasis. We describe how the analysis, retrieval and delivery phases contribute making spoken audio content more accessible, and we outline a number of outstanding research issues. We also discuss the main application domains and try to identify important issues for future developments. The structure of the article is based on general system architecture for content-based 2 access which is depicted in Figure 1. Although the tasks within each processing stage may appear unconnected, the interdependencies and the sequence with which they take place vary

In this paper we seek to explore the interaction between the style of a broadcast news story and its summarization technique. We report the performance of three different summarization techniques on broadcast news stories, which are split into planned speech and spontaneous speech. The initial results indicate that some summarization techniques work better for the documents with spontaneous speech than for those with planned speech. Even for human beings some documents are inherently difficult to summarize. We observe this correlation between degree of difficulty in summarizing and performance of the three automatic summarizers. Given the high frequency of named entities in broadcast news and even greater number of references to these named entities, we also gauge the effect of named entity and coreference resolution in a news story, on the performance of these summarizers. 1.

We present two approaches to email thread summarization: Collective Message Summarization (CMS) applies a multi-document summarization approach, while Individual Message Summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate these ideas on the Enron collection—a very challenging corpus because of the highly technical language. Experimental results point to two findings: that CMS represents a better approach to email thread summarization, and that current sentence compression techniques do not improve summarization performance in this genre. 1

Multimedia meeting collections, composed of unedited audio and video streams, handwritten notes, slides, and electronic documents that jointly constitute a raw record of complex human interaction processes in the workplace, have attracted interest due to the increasing feasibility of recording them in large quantities, by the opportunities for information access and retrieval applications derived from the automatic extraction of relevant meeting information, and by the challenges that the extraction of semantic information from real human activities entails. In this paper, we present a succint overview of recent approaches in this field, largely influenced by our own experiences. We first review some of the existing and potential needs for users of multimedia meeting information systems. We then summarize recent work on various research areas addressing some of these requirements. In more detail, we describe our work on automatic analysis of human interaction patterns from audio-visual sensors, discussing open issues in this domain.