Data-driven Summarization of Scientific Articles

Understand

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.