Automatic Text Summarization on Social Media

In the Natural Language Processing (NLP) community, automatic text summarization is considered to be a very difficult problem. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of information. A good text summarization system should understand the whole text, reorganize information, and generate coherent, informative and remarkably short summaries to convey the important information of the original text. In this paper, an innovative text summarization model has been constructed, which combines BERT, reinforcement learning, sequence-to-sequence and other technologies. Our model is evaluated on the LCSTS[1] dataset, which is a high-quality corpus of Chinese short text summarization dataset constructed from "Sina Weibo", The experiment shows that our method has made a great breakthrough in Rouge Scores compared with other researches.