Embedding-based Scientific Literature Discovery in a Text Editor Application

Onur Gökçe, Jonathan Prada, Nikola I. Nikolov, Nianlong Gu, Richard H.R. Hahnloser


Abstract
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.
Anthology ID:
2020.acl-demos.36
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
320–326
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-demos.36
DOI:
10.18653/v1/2020.acl-demos.36
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-demos.36.pdf
Video:
 http://slideslive.com/38928612