Summarization: Using MMR for Diversity-Based Reranking and Evaluating Summaries

Abstract

This paper 1 develops a method for combining queryrelevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in reranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in ad-hoc query and in single document summarization. The latter are borne out by the trial-run (unofficial) TREC-style evaluation of summarization systems. However, the clearest advantage is demonstrated in the automated construction of large document and non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection. This paper also discusses our preliminary evaluation of summarization methods for single documents.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADA631230

Entities

People

  • Jade Goldstein
  • Jaime Carbonell

Organizations

  • Carnegie Mellon University

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Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Automated Text Summarization
  • Computational Fluid Dynamics
  • Data Sets
  • Differential Equations
  • Governments
  • Information Retrieval
  • International Relations
  • Law
  • Natural Language Processing
  • Prejudice
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Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Information Retrieval
  • Systems Analysis and Design