1999 AdvancesInAutoTextSum

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Subject Headings: Text Summarization Task, Text Summarization Algorithm

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Cited By

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Book Overview

With the rapid growth of the World Wide Web and electronic information services, information is becoming available on-line at an incredible rate. One result is the oft-decried information overload. No one has time to read everything, yet we often have to make critical decisions based on what we are able to assimilate. The technology of automatic text summarization is becoming indispensable for dealing with this problem. Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task.

Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. [1999_AdvancesInAutoTextSum|This book]] presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections: Classical Approaches, Corpus-based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas.

Table of Contents

Preface Introduction

  • 1 Automatic Summarizing: Factors and Directions - K. Spärck-Jones Classical Approaches
  • 2 The Automatic Creation of Literature Abstracts - H. P. Luhn
  • 3 New Methods in Automatic Extracting - H. P. Edmundson
  • 4 Automatic Abstracting Research at Chemical Abstracts Service - J. J. Pollock and A. Zamora

Corpus-based Approaches

  • 5 A Trainable Document Summarizer - J. Kupiec, J. Pedersen, and F. Chen
  • 6 Development and Evaluation of a Statistically Based Document Summarization System - S. H. Myaeng and D. Jang
  • 7 A Trainable Summarizer with Knowledge Acquired from Robust NLP Techniques - C. Aone, M. E. Okurowski, J. Gorlinsky, and B. Larsen
  • 8 Automated Text Summarization in SUMMARIST - Eduard Hovy and C. Lin

Exploiting Discourse Structure

  • 9 Salience-based Content Characterization of Text Documents B. Boguraev and C. Kennedy
  • 10 Using Lexical Chains for Text Summarization - Regina Barzilay and M. Elhadad
  • 11 Discourse Trees Are Good Indicators of Importance in Text - D. Marcu
  • 12 A Robust Practical Text Summarizer - T. Strzalkowski, G. Stein, J. Wang, and B. Wise
  • 13 Argumentative Classification of Extracted Sentenses as a First Step Towards Flexible Abstracting - S. Teufel and M. Moens

Knowledge-rich Approaches

  • 14 Plot Units: A Narrative Summarization Strategy - W. G. Lehnert
  • 15 Knowledge-based text Summarization: Salience and Generalization Operators for Knowledge Base Abstraction - U. Hahn and U. Reimer
  • 16 Generating Concise Natural Language Summaries - K. McKeown, J. Robin, and K. Kukich
  • 17 Generating Summaries from Event Data - M. Maybury

Evaluation Methods

  • 18 The Formation of Abstracts by the Selection of Sentences - G. J. Rath, A. Resnick, and T. R. Savage
  • 19 Automatic Condensation of Electronic Publications by Sentence Selection - R. Brandow, K. Mitze, and L. F. Rau
  • 20 The Effects and Limitations of Automated Text Condensing on Reading Comprehension Performance - A. H. Morris, G. M. Kasper, and D. A. Adams
  • 21 An Evaluation of Automatic Text Summarization Systems - T. Firmin and M J. Chrzanowski

New Summarization Problem Areas

  • 22 Automatic Text Structuring and Summarization - G. Salton, A. Singhal, M. Mitra, and C. Buckley
  • 23 Summarizing Similarities and Differences among Related Documents - I. Mani and E. Bloedorn
  • 24 Generating Summaries of Multiple News Articles - K. McKeown and D. R. Radev
  • 25 An Empirical Study of the Optimal Presentation of Multimedia Summaries of Broadcast News - A Merlino and M. Maybury
  • 26 Summarization of Diagrams in Documents - R. P. Futrelle

References


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1999 AdvancesInAutoTextSumAdvances in Automatic Text SummarizationMIT Presshttp://books.google.com/books?id=YtUZQaKDmzEC1999