@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://aclanthology.org/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}",
}
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%0 Conference Proceedings
%T SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
%A Gu, Nianlong
%A Hahnloser, Richard H.R.
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gu-hahnloser-2023-scilit
%X 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 https://scilit.vercel.app
%R 10.18653/v1/2023.acl-demo.22
%U https://aclanthology.org/2023.acl-demo.22
%U https://doi.org/10.18653/v1/2023.acl-demo.22
%P 235-246
Markdown (Informal)
[SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation](https://aclanthology.org/2023.acl-demo.22) (Gu & Hahnloser, ACL 2023)
ACL