• A framework for summarizing a corpus of evaluative documents about a single entity by a natural language summary
  • Series of previous work by same first author
    • first paper: extract & organize evaluative info
    • 1st study: compared the two summarizers: extractive vs. abstractive. The summarizers have different strengths and weaknesses.
    • 2nd study: when corpus is controversial, Evaluative text abstraction tends to be more effective than extraction.
    • 3rd study: assessed the effectiveness of our user tailoring strategy. User tailored summaries are more informative than un-tailored ones.


  • a task of annual Document Understanding Conference, DUC (renamed to Text Analysis Conference (TAC) since 2008)
  • most of previous work focused on factual, edited text and biographies, for which the goal is extract & order the most informative sentences in a domain-independent way, while avoiding repetition
  • when documents contain inconsistent info, the summarizer needs to identify overlaps and inconsistencies and produce summary that explains those inconsistencies.
  • basic steps
    • extract & analyze the evaluations expressed in the corpus (been studied extensively)
    • summarize them (received less attention)
      • extraction-based summarizer: extract
  • a
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