Modeling and Summarizing News Events Using Semantic Triples

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, 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 that 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 the DUC'04 and DUC'07 datasets showing that our approach outperforms baseline approaches by a large margin in terms of ROUGE and PYRAMID scores.