Research

Summaries for trending news topics are often created from one or more news articles. In this paper we explore using relevant Facebook public posts in addition to the news articles to improve summarization of trending news. We propose different approaches incorporating information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge’s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments show that relevant Facebook public posts provide useful information and can be effectively leveraged to improve summarization results.