Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes in large and rapidly evolving fields. By making these processes inter-operable, natural language processing (NLP) provides opportunities for creating end-to-end assistive writing tools. We propose SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper, taking into consideration the user-provided context and keywords. SciLit efficiently recommends papers from large databases of hundreds of millions of papers using a two-stage pre-fetching and re-ranking literature search system that flexibly deals with addition and removal of a paper database. We provide a convenient user interface that displays the recommended papers as extractive summaries and that offers abstractively-generated citing sentences which are aligned with the provided context and which mention the chosen keyword(s). Our assistive tool for literature discovery and scientific writing is available at https://scilit.vercel.app

翻译：科学写作涉及检索、总结与引用相关论文，在规模庞大且快速发展的领域中，这些过程往往耗时费力。自然语言处理通过实现这些过程的互操作性，为创建端到端辅助写作工具提供了契机。本文提出SciLit流水线，该流水线能自动推荐相关论文、提取要点，并基于用户提供的上下文和关键词生成引用语句中的参考句子。其采用预取-重排序两阶段文献检索系统，灵活支持数十亿级论文数据库的增删操作，实现高效推荐。我们提供了便捷的用户界面，既以抽取式摘要形式展示推荐论文，又提供与给定上下文对齐、提及所选关键词的生成式引文语句。该文献发现与科学写作辅助工具现已开放访问：https://scilit.vercel.app