Modeling and Summarizing News Events using Semantic Triples

author: Radityo Eko Prasojo, KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano
published: July 10, 2018,   recorded: June 2018,   views: 11
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Summarizing news articles is becoming crucial for allowing quick and concise access to information about daily events. This task can be challenging when the same event is reported with various levels of detail or is subject to diverse view points. A well established technique in the area of news summarization consists in modeling events as a set of semantic triples. These triples are weighted, mainly based on their frequencies, and then fused to build summaries. Typically, these triples are extracted from main clauses which might lead to information loss. Moreover, some crucial facets of news, such as reasons or consequences, are mostly reported in subordinate clauses and thus, they are not properly handled. In this paper, we focus on an existing work that uses a graph structure to model sentences allowing the access to any triple independently from the clause it belongs to. Summary sentences are then generated by taking the top ranked paths that contain many triples and show grammatical correctness. We further provide several improvements to such approach. First, we leverage node degrees for finding the most important triples and facets shared among sentences. Second, we enhance the process of triple fusion by providing more effective similarity measures that exploit entity linking and predicate similarity. We performed extensive experiments using DUC2004 and DUC2007 datasets showing that our approach outperforms baseline approaches by a large margin in terms of ROUGE and PYRAMID scores.

See Also:

Download slides icon Download slides: eswc2018_eko_prasojo_semantic_triples_01.pdf (2.1┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: