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

TL;DR

SciLit is an NLP-powered platform that streamlines scientific literature discovery, summarization, and citation generation by recommending relevant papers, extracting highlights, and suggesting citation sentences in an integrated manner.

Contribution

The paper introduces SciLit, a novel end-to-end system combining literature recommendation, summarization, and citation suggestion with a scalable search architecture.

Findings

Efficient retrieval from large paper databases using a two-stage search system.

Generation of context-aligned, extractive summaries and citation sentences.

User-friendly interface facilitating scientific writing and literature review.

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 https://scilit.vercel.app