[2306.03535] SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation
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Computer Science > Computation and Language

arXiv:2306.03535 (cs)
[Submitted on 6 Jun 2023 (v1), last revised 6 Nov 2023 (this version, v2)]

Title:SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation

Authors:Nianlong Gu, Richard H.R. Hahnloser
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Abstract: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 this https URL
Comments: Accepted at ACL 2023 System Demonstration
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2306.03535 [cs.CL]
  (or arXiv:2306.03535v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2306.03535
Related DOI: https://doi.org/10.18653/v1/2023.acl-demo.22

Submission history

From: Nianlong Gu [view email]
[v1] Tue, 6 Jun 2023 09:34:45 UTC (4,405 KB)
[v2] Mon, 6 Nov 2023 15:53:23 UTC (4,405 KB)
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