Event Identification as a Decision Process with Non-linear Representation of Text

We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents. In general, sfIN first encodes a document into multi-scale memory stacks, then extracts special events via conducting multi-scale actions, which can be considered as a special type of sequence labelling. The design of large scale actions makes it more efficient processing a long document. The whole model is trained with both supervised learning and reinforcement learning.Comment: 8 pages, 8 figure

LCSTS: A Large Scale Chinese Short Text Summarization Dataset

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.Comment: Recently, we received feedbacks from Yuya Taguchi from NAIST in Japan and Qian Chen from USTC of China, that the results in the EMNLP2015 version seem to be underrated. So we carefully checked our results and find out that we made a mistake while using the standard ROUGE. Then we re-evaluate all methods in the paper and get corrected results listed in Table 2 of this versio