Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization

TL;DR

This paper demonstrates how domain-specific fine-tuning of Denoising Sequence-to-Sequence models like BART significantly improves summarization performance in specialized fields such as medicine and finance, with a practical pipeline for implementation.

Contribution

The authors introduce a domain-specific fine-tuning approach for BART that enhances summarization accuracy, providing a pipeline adaptable to various specialized domains.

Findings

5-6% absolute ROUGE-1 improvement over baseline

Effective data augmentation strategies for domain adaptation

Open-source pipeline for domain-specific summarization

Abstract

Summarization of long-form text data is a problem especially pertinent in knowledge economy jobs such as medicine and finance, that require continuously remaining informed on a sophisticated and evolving body of knowledge. As such, isolating and summarizing key content automatically using Natural Language Processing (NLP) techniques holds the potential for extensive time savings in these industries. We explore applications of a state-of-the-art NLP model (BART), and explore strategies for tuning it to optimal performance using data augmentation and various fine-tuning strategies. We show that our end-to-end fine-tuning approach can result in a 5-6\% absolute ROUGE-1 improvement over an out-of-the-box pre-trained BART summarizer when tested on domain specific data, and make available our end-to-end pipeline to achieve these results on finance, medical, or other user-specified domains.