@inproceedings{gu-hahnloser-2023-scilit,
    title = "{S}ci{L}it: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation",
    author = "Gu, Nianlong  and
      Hahnloser, Richard H.R.",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.acl-demo.22/",
    doi = "10.18653/v1/2023.acl-demo.22",
    pages = "235--246",
    abstract = "Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes. Although in many workflows these processes are serially linked, there are opportunities for natural language processing (NLP) to provide end-to-end assistive 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 \url{https://scilit.vercel.app}"
}