Editors-in-the-loop News Article Summarization Framework with Sentence Selection and Compression

This paper proposes a human-in-the-loop framework to summarize news articles by sentence selection and compression. The system enumerates summary candidates by selecting N sentences representing the article based on a quantitative metric and then compressing each sentence by syntactic analysis. Experiments showed that the proposed system was able to extract the same topics as the human editor's work at the rate of 26 %. Even though the rate was not high enough, the proposed framework has the advantage that it is easy to incorporate the editor's intentions in the sentence selection and compression by giving weights. This approach not only has the potential to reduce the burden on editors, but can also contribute to giving them a new perspective in creating summaries.