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、总结和引用相关论文，在规模庞大且快速发展的领域中，这些过程可能耗时巨大。通过使这些过程相互可操作，自然语言处理（NLP）为创建端到端的辅助写作工具提供了机遇。我们提出了SciLit，一个能够自动推荐相关论文、提取要点，并基于用户提供的上下文和关键词建议参考句子作为论文引文的流水线系统。SciLit利用两阶段预取与重排序的文献检索系统，从包含数亿篇论文的大型数据库中高效推荐论文，该检索系统能够灵活处理论文数据库的新增与移除。我们提供了一个便捷的用户界面，以抽取式摘要的形式展示推荐论文，同时提供与给定上下文对齐、并提及所选关键词的抽象式生成引文句子。我们的文献发现与科学写作辅助工具备于https://scilit.vercel.app。