#GENERAL

PDF
Journal of Computational Intelligence
University of British Columbia Vancouver, Canada
First Author: Giuseppe Carenini, Associate Professor of Computer Science, University of British Columbia
Work series on same topic of Giuseppe Carenini
- 》》MULTI-DOCUMENT SUMMARIZATION OF EVALUATIVE TEXT, journal version, Journal of Computational Intelligence 2013
- 》》Extractive vs. NLG-based abstractive summarization of evaluative text: The effect of corpus controversiality, INLG 2008 (rank B/B2)
- 》》Multi-Document Summarization of Evaluative Text, EACL 2006 (rank A/A2)
- 》》Extracting knowledge from evaluative text, K-Cap 2005 (rank A/B1), 196 citations

#ABSTRACT

PROBLEM
- 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.

#TASK of TEXT SUMMARIZATION

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