TWEETSUM: Event oriented Social Summarization Dataset
Ruifang He, Liangliang Zhao, Huanyu Liu · 2020
With social media becoming popular, a vast of short and noisy messages are produced by millions of users when a hot event happens.Developing social summarization systems becomes more and more critical for people to quickly grasp core and essential information.However, the publicly available and high-quality large scale dataset under social media situation is rare.Constructing such corpus is not easy and very expensive since short texts have very complex social characteristics.Though there exist some datasets, they only consider the text on social media and ignore the potential user relations relevant signals on social network.In this paper, we construct TWEETSUM, a new event-oriented dataset for social summarization.The original data is collected from twitter and contains 12 real world hot events with a total of 44,034 tweets and 11,240 users.We create expert summaries for each event, and we also have the annotation quality evaluation.In addition, we collect additional social signals (i.e.user relations, hashtags and user profiles) and further establish user relation network for each event.To our knowledge, it is the first event-oriented social summarization dataset that contains social relationships.Besides the detailed dataset description, we show the performance of several typical extractive summarization methods on TWEETSUM to establish baselines.For further researches, we will release this dataset to the public.