Long document (e.g., scientific papers) summarization is obtaining more and more attention in recent years. Extractive approaches attempt to choose salient sentences via understanding the whole document, but long documents cover numerous subjects with varying details and will not ease content understanding. Instead, abstractive approaches elaborate to generate related tokens while suffering from truncating the source document due to their input sizes. To this end, we propose a Simple yet Effective HYbrid approach, which we call SEHY, that exploits the discourse information of a document to select salient sections instead sentences for summary generation. On the one hand, SEHY avoids the full-text understanding; on the other hand, it retains salient information given the length limit. In particular, we design two simple strategies for training the extractor: extracting sections incrementally and based on salience-analysis. Then, we use strong abstractive models to generate the final summary. We evaluate our approach on a large-scale scientific paper dataset: arXiv. Further, we discuss how the disciplinary class (e.g., computer science, math or physics) of a scientific paper affects the performance of SEHY as its writing style indicates, which is unexplored yet in existing works. Experimental results show the effectiveness of our approach and interesting findings on arXiv and its subsets generated in this paper.

Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other hand, intensive interaction of attention on Transformer-based models like BERT may hinder performance improvement. To alleviate these problems, we build a new Chinese microblog dataset named Weibo20 by collecting posts and associated comments from Sina Weibo and propose a new ensemble named STANKER (Stacking neTwork bAsed-on atteNtion-masKed BERT). STANKER adopts two level-grained attention-masked BERT (LGAM-BERT) models as base encoders. Unlike the original BERT, our new LGAM-BERT model takes comments as important auxiliary features and masks co-attention between posts and comments on lower-layers. Experiments on Weibo20 and three existing social media datasets showed that STANKER outperformed all compared models, especially beating the old state-of-the-art on Weibo dataset.