# Structured Summarization of Academic Publications

## TL;DR

This paper introduces SUSIE, a new method for structured summarization of academic papers, along with PMC-SA, a dataset for training and evaluating such models, demonstrating significant performance improvements.

## Contribution

The paper presents SUSIE, a novel summarization approach compatible with current models, and introduces PMC-SA, a new dataset for structured scientific summarization.

## Key findings

- SUSIE improves summarization performance by up to 4 ROUGE points.
- The PMC-SA dataset is suitable for neural network-based structured summarization.
- Applying SUSIE enhances various summarization models on academic articles.

## Abstract

We propose SUSIE, a novel summarization method that can work with state-of-the-art summarization models in order to produce structured scientific summaries for academic articles. We also created PMC-SA, a new dataset of academic publications, suitable for the task of structured summarization with neural networks. We apply SUSIE combined with three different summarization models on the new PMC-SA dataset and we show that the proposed method improves the performance of all models by as much as 4 ROUGE points.