Query-based opinion summarization for legal blog entries

We present the first report of automatic sentiment summarization in the legal domain. This work is based on processing a set of legal questions with a system consisting of a semi-automatic Web blog search module and FastSum, a fully automatic extractive multi-document sentiment summarization system. We provide quantitative evaluation results of the summaries using legal expert reviewers. We report baseline evaluation results for query-based sentiment summarization for legal blogs: on a five-point scale, average responsiveness and linguistic quality are slightly higher than 2 (with human inter-rater agreement at κ = 0.75). To the best of our knowledge, this is the first evaluation of sentiment summarization in the legal blogosphere. Categories and Subject Descriptors H.3.0.a [Information Storage and Retrieval]: General—Question Answering and Summarization; I.2.7.i [Information Storage and Retrieval]: Natural Language Processing—Opinion Mining General Terms Modeling, Experimenta...

• Type: Conference
• Year: 2009
• Where: ICAIL
• Authors: Jack G. Conrad, Jochen L. Leidner, Frank Schilder, Ravi Kondadadi
• Keywords: Artificial Intelligence, Automatic Sentiment Summarization, ICAIL 2009, Multi-document Sentiment Summarization, Sentiment Summarization