Automatic Title Generation for Spoken Broadcast News

Abstract

ABSTRACT In this paper, we implemented a set of title generation methods using training set of 21190 news stories and evaluated them on an independent test corpus of 1006 broadcast news documents, comparing the results over manual transcription to the results over automatically recognized speech. We use both F1 and the average number of correct title words in the correct order as metric. Overall, the results show that title generation for speech recognized news documents is possible at a level approaching the accuracy of titles generated for perfect text transcriptions.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2001
Accession Number
ADA458632

Entities

People

  • Alexander G. Hauptmann
  • Rong Jin

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Applied Computer Science
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • Automated Text Summarization
  • Bayesian Networks
  • Computer Science
  • Demographic Cohorts
  • Education
  • Language
  • Learning
  • Machine Learning
  • Natural Language Processing
  • Statistics
  • Training

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • International Journalism and Media Studies.
  • Speech Processing/Speech Recognition.