Data-driven Summarization of Scientific Articles

By Nikola I. Nikolov, Michael Pfeiffer, Richard H. R. Hahnloser. 2018

44 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Summarization

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yejin Choi
2. Noah A. Smith
3. Mohit Bansal
4. Luke S. Zettlemoyer
5. Hannaneh Hajishirzi
6. Asli Celikyilmaz
7. Jianfeng Gao
8. Percy Liang

In Summarization

1. Iwona Mochol
2. Yejin Choi
3. Aleksander Wawer
4. Bogdan Gliwa
5. Maciej Biesek

Citations in the corpus

• SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (2019, background): “Major research efforts have focused so far on summarization of single-speaker documents like news (e.g., Nallapati et al. (2016)) or sci-entific publications (e.g., Nikolov et al. (2018)).”
• Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow (2019, background): “Generation with Narrative Flow Due to the need for accurate understanding of long-distance dependencies and narrative structure, modeling coherent narrative flow has proved to be a major challenge in the field of text generation, particularly for scientific documents (Koncel-Kedziorski et al., 2019;”
• TLDR: Extreme Summarization of Scientific Documents (2020, background): “, 2018), and other techniques focusing on exploiting the unique properties of scientific documents such as long length and structure (Conroy and Davis, 2017; Nikolov et al., 2018; Cohan et al., 2018; Xiao and Carenini, 2019).”