Lightweight contrastive summarization for news comment mining

Authors:
Gobaan Raveendran;Charles L.A. Clarke
Affiliations:
University of Waterloo, Waterloo, ON, Canada;University of Waterloo, Waterloo, ON, Canada
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

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Abstract

We develop and discuss a news comment miner that presents distinct viewpoints on a given theme or event. Given a query, the system uses metasearch techniques to find relevant news articles. Relevant articles are then scraped for both article content and comments. Snippets from the comments are sampled and presented to the user, based on theme popularity and contrastiveness to previously selected snippets. The system design focuses on being quicker and more lightweight than recent topic modelling approaches, while still focusing on selecting orthogonal snippets.