A Summarization System for Scientific Documents
Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat, Ora Peled Nakash,, Odellia Boni, Haggai Roitman, Doron Cohen, Bar Weiner, Yosi Mass, Or Rivlin,, Guy Lev, Achiya Jerbi, Jonathan Herzig, Yufang Hou, Charles Jochim, Martin, Gleize, Francesca Bonin, and David Konopnicki
TL;DR
This paper introduces a new system that retrieves and summarizes scientific documents in computer science, enhancing discovery and understanding through user-centered design and validation with experts.
Contribution
It presents a novel summarization system tailored for scientific papers, built on a large dataset and validated through user studies and expert evaluation.
Findings
System ingested 270,000 papers
Summaries are concise yet detailed
Validated by human experts
Abstract
We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.
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