Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of the research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum data set, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR F1-score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub data set, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR F1-score of 24.00, and BERTScore F1 of 85.25, outperforming other models.

翻译：当前，许多研究论文在正文前附有"研究亮点"，用以概括论文的核心发现。这些亮点不仅能帮助研究者精准、快速地识别论文贡献，还能通过搜索引擎提升文章的可发现性。本文旨在根据论文的特定段落自动构建研究亮点。我们采用带有覆盖机制的指针生成网络，并在输入层引入上下文嵌入模块，将输入令牌编码为SciBERT嵌入。我们在基准数据集CSPubSum上测试了模型，并提出了MixSub——一个面向自动研究亮点生成的新型多学科论文语料库。在CSPubSum与MixSub两个数据集上，与相关变体及文献中其他模型相比，所提模型均取得了最优性能。在CSPubSum数据集中，当输入仅为论文摘要而非其他段落时，模型表现最佳：ROUGE-1、ROUGE-2、ROUGE-L的F1值分别达到38.26、14.26和35.51，METEOR F1值为32.62，BERTScore F1值为86.65，全面超越所有基线模型。在新提出的MixSub数据集上（仅以摘要为输入），所提模型（采用全训练语料统一训练，未区分学科类别）的ROUGE-1、ROUGE-2、ROUGE-L的F1值分别达到31.78、9.76和29.3，METEOR F1值为24.00，BERTScore F1值为85.25，同样优于其他模型。