Structured Text Summarization Via Open Domain Information Extraction

Given the dramatic growth of digital content, new solutions are needed for us to be able to get a quick overview of pertinent information without being inundated by irrelevant details. While there has been ample research on automatic summarization methods, summaries may still be somewhat convoluted and hard to absorb. In this paper, we propose the novel task of structured text summarization, which we address by combining ranking techniques with open information extraction. This method collaborates with linguistics and yields an uncluttered, more easily digestible overview of key insights from a text and performs very well in certain aspect in our experiments.