Paper Info
Title
Mediagist: A Cross-Lingual Analyser Of Aggregated News And Commentaries
Abstract
We introduce MediaGist, an online system for crosslingual analysis of aggregated news and commentaries based on summarization and sentiment analysis technologies. It is designed to assist journalists to detect and explore news topics, which are controversially reported or discussed in different countries. News articles from current week are clustered separately in currently 5 languages and the clusters are then linked across languages. Sentiment analysis provides a basis to compute controversy scores and summaries help to explore the differences. Recognized entities play an important role in most of the system's modules and provide another way to explore the data. We demonstrate the capabilities of MediaGist by listing highlights from the last week and present a rough evaluation of the system.
Year
Venue
DocType
2016
PROCEEDINGS OF 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL-2016): SYSTEM DEMONSTRATIONS
Conference
Volume
Citations 
PageRank 
P16-4
0
0.34
References 
Authors
5
1
Name
Order
Citations
PageRank
josef steinberger135526.95