| Title |
Data-driven Summarization of Scientific Articles |
| Authors |
Nikola Nikolov, Michael Pfeiffer, Richard Hahnloser |
| 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. |
Full paper |
Data-driven Summarization of Scientific Articles
|
| Bibtex |
@InProceedings{NIKOLOV18.2,
author = {Nikola Nikolov ,Michael Pfeiffer and Richard Hahnloser},
title = {Data-driven Summarization of Scientific Articles},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {may},
date = {7-12},
location = {Miyazaki, Japan},
editor = {},
publisher = {European Language Resources Association (ELRA)},
address = {Paris, France},
isbn = {979-10-95546-20-7},
language = {english}
} |
Summary of the paper