# Embedding-based Scientific Literature Discovery in a Text Editor Application

arXiv: https://arxiv.org/abs/2005.04961 · HTML: https://mlsanity.com/paper/2005.04961/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 3 of 3)

- [Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking](https://mlsanity.com/paper/2112.01206/) (2021)
- [SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation](https://mlsanity.com/paper/2306.03535/) (2023)
- [MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions](https://mlsanity.com/paper/2408.14623/) (2024)

## Abstract (arXiv)

Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field. Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process. Here, we present a web application that combines text editing and literature discovery in an interactive user interface. The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author's manuscript and his interests. Our application aims to take a step towards more enjoyable and effortless academic writing. The demo of the application (https://SciEditorDemo2020.herokuapp.com/) and a short video tutorial (https://youtu.be/pkdVU60IcRc) are available online.