Thomson Reuters at TAC 2008: Aggressive Filtering with FastSum for Update and Opinion Summarization.
Published 1 January 2008
Frank Schilder, Ravi Kondadadi, Jochen L. Leidner, Jack G. Conrad
Citations14
SJR quartileQ2
SJR score0.56
SNIP0.86
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TL;DR
This work shows that a classifier that identifies sentences that are similar to typical first sentences of a news article improves the overall linguistic quality of the generated summaries in the Update Summarization task.
Abstract
In TAC 2008 we participated in the main task (Update Summarization) as well as the Sentiment Summarization pilot task. We modified the FastSum system (Schilder and Kondadadi, 2008) and added more aggressive filtering in order to adapt the system to update summarization and sentiment summarization. For the Update Summarization task, we show that a classifier that identifies sentences that are similar to typical first sentences of a news article improves the overall linguistic quality of the generated summaries. For the Sentiment Summarization pilot task, we use a simple sentiment classifier based on a gazetteer of positive and negative sentiment words derived from the General Inquirer and other sources to produce opinion-based summaries for a collection of blog posts given a set of positive and negative questions. 1
Keywords
Computer Science
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