Paper Info
Title
Lightweight contrastive summarization for news comment mining
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.
Year
DOI
Venue
2012
10.1145/2348283.2348490
SIGIR
Keywords
Field
DocType
metasearch technique,article content,theme popularity,news comment mining,orthogonal snippet,relevant news article,news comment miner,system design,lightweight contrastive summarization,recent topic,relevant article,distinct viewpoint,summarization,opinion
Data mining,Automatic summarization,Metasearch engine,World Wide Web,Information retrieval,Viewpoints,Computer science,Popularity,Systems design,Topic model
Conference
Citations 
PageRank 
References 
2
0.38
5
Authors
2
Name
Order
Citations
PageRank
Gobaan Raveendran120.38
Charles L.A. Clarke23289286.78