论文标题
Scibertsum：科学文档的提取性摘要
SciBERTSUM: Extractive Summarization for Scientific Documents
论文作者
论文摘要
摘要文献的重点是新闻文章的摘要。 CNN-DailyMail中的新闻文章是相对较短的文档，平均每个文件约30个句子。我们介绍了Scibertsum，这是我们的摘要框架，旨在汇总长文件，例如有500多个句子的科学论文。 Scibertsum将Bertsum扩展到长文档，通过1）添加一个嵌入层以在句子向量中包括部分信息，以及2）应用一个稀疏的注意机制，每个句子都会在本地进入附近的句子，并且只有少数句子在全球范围内参加所有其他句子。我们使用了科学论文作者生成的幻灯片作为参考总结，因为它们包含了本文中的技术细节。结果表明，在胭脂分数方面，我们的模型具有优势。
The summarization literature focuses on the summarization of news articles. The news articles in the CNN-DailyMail are relatively short documents with about 30 sentences per document on average. We introduce SciBERTSUM, our summarization framework designed for the summarization of long documents like scientific papers with more than 500 sentences. SciBERTSUM extends BERTSUM to long documents by 1) adding a section embedding layer to include section information in the sentence vector and 2) applying a sparse attention mechanism where each sentences will attend locally to nearby sentences and only a small number of sentences attend globally to all other sentences. We used slides generated by the authors of scientific papers as reference summaries since they contain the technical details from the paper. The results show the superiority of our model in terms of ROUGE scores.
