SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
This tool assists researchers in scientific writing by integrating literature discovery, summarization, and citation generation, though it is incremental as it combines existing NLP methods into a unified platform.
The authors tackled the time-consuming tasks of retrieving, summarizing, and citing scientific papers by developing SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests citation sentences based on user context and keywords, with a system capable of handling databases of hundreds of millions of papers.
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