Data-driven Summarization of Scientific Articles

TL;DR

This paper introduces new datasets derived from scientific articles for training and evaluating multi-sentence summarization models, demonstrating their suitability and providing benchmarks for long-sequence summarization tasks.

Contribution

It creates two large-scale multi-sentence summarization datasets from scientific articles and evaluates existing neural models on these datasets, highlighting their potential.

Findings

Scientific articles are suitable for data-driven summarization.

Existing models perform well on scientific article datasets.

The datasets serve as benchmarks for long-sequence summarization.

Abstract

Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels - the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences.