》》MULTI-DOCUMENT SUMMARIZATION OF EVALUATIVE TEXT, journal version
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
- 》》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
-
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
- extract & analyze the evaluations expressed in the corpus (been studied extensively)
- summarize them (received less attention)
- extraction-based summarizer: extract
Author And Source
この問題について(》》MULTI-DOCUMENT SUMMARIZATION OF EVALUATIVE TEXT, journal version), 我々は、より多くの情報をここで見つけました https://qiita.com/badrabbit/items/6eeee8a5cc34043f5b65著者帰属：元の著者の情報は、元のURLに含まれています。著作権は原作者に属する。
Content is automatically searched and collected through network algorithms . If there is a violation . Please contact us . We will adjust (correct author information ,or delete content ) as soon as possible .