Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: e proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diusion is known as early rumor detection, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. us, identifying trending rumors demands an ecient yet exible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classication algorithms to rumor detection in earliness since they rely on hand-craed features which require intensive manual eorts in the case of large amount of posts. is paper presents a deep aention model on the basis of recurrent neural networks (RNN) to learn selectively temporal hidden representations of sequential posts for identifying rumors. e proposed model delves so-aention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep aention based RNN model outperforms state-of-thearts that rely on hand-craed features; (2) the introduction of so aention mechanism can eectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection