GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents

Sajad Sotudeh Gharebagh, Arman Cohan, Nazli Goharian

First Workshop on Scholarly Document Processing (SDP 2020) Workshop Paper

You can open the pre-recorded video in a separate window.

Abstract: This paper presents our methods for the LongSumm 2020: Shared Task on Generating Long Summaries for Scientific Documents, where the task is to generatelong summaries given a set of scientific papers provided by the organizers. We explore 3 main approaches for this task: 1. An extractive approach using a BERT-based summarization model; 2. A two stage model that additionally includes an abstraction step using BART; and 3. A new multi-tasking approach on incorporating document structure into the summarizer. We found that our new multi-tasking approach outperforms the two other methods by large margins. Among 9 participants in the shared task, our best model ranks top according to Rouge-1 score (53.11%) while staying competitive in terms of Rouge-2.
NOTE: Video may display a random order of authors. Correct author list is at the top of this page.