S ci L it: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation (2023)

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 https://scilit.vercel.app

## introduction when we compose sentences like "our experiments show that xxx performs significantly worse than yyy" in a manuscript, we may want to find papers that report similar performance evaluations @xcite and discuss these in our manuscript. this process is a non-trivial task requiring in-depth human involvement in finding, summarizing, and citing papers, which raises the question whether it is possible to partly automate this process to reduce users' cognitive load in searching, retrieving, reading, and rephrasing related findings. recent advances in natural language processing (nlp) help answer this question. first, releases of large scientific corpora such as s2orc @xcite and general index @xcite provide opportunities for building large databases of scientific ## evaluation in this section, we first show how scilit works and then we evaluate its performance. ## related work recently, ai-driven platforms focused on literature recommendation and scientific paper summarization have been proposed. (keywords: platform, paper: #2) one such platform is ai research navigator @xcite , which combines classical keyword search with neural retrieval to discover and organize relevant literature. (keywords: scientific; summarization; platform, paper #3) another platform is anne o'tate, which supports user-driven summarization, drill-down and mining of search results from pubmed, the leading search engine for biomedical literature @xcite . (keywords: related work generation, paper #9) chen and zhuge (2019) automatically generates related work by comparing the main text of the paper being written with the citations of other papers that cite the same references. in the previous paragraph, the italicized citation sentences are generated from scilit. in generating each sentence, we use all the preceding sentences in the paragraph as contexts and use the keywords in parentheses to obtain the recommended papers and the corresponding citation sentences. the paper index in parentheses indicates the ranked order of recommended papers. ## conclusion and future work this paper demonstrates scilit, a platform for joint scientific literature retrieval, paper summarization, and citation generation. scilit can efficiently recommend papers from hundreds of millions of papers and proactively provides highlights and suggested citations to assist authors in reading and discussing the scientific literature. in addition, our prefetching, reranking, and citation generation system can be conditioned on user-provided keywords, which provides flexibility and adjusts the platform's response to user intention. in the future, we will further improve the performance of each module, especially the citation generation part, and collect feedback from users to improve the overall workflow and the frontend user experience.