Low-Resource Neural Headline Generation

Ottokar Tilk, Tanel Alumäe


Abstract
Recent neural headline generation models have shown great results, but are generally trained on very large datasets. We focus our efforts on improving headline quality on smaller datasets by the means of pretraining. We propose new methods that enable pre-training all the parameters of the model and utilize all available text, resulting in improvements by up to 32.4% relative in perplexity and 2.84 points in ROUGE.
Anthology ID:
W17-4503
Volume:
Proceedings of the Workshop on New Frontiers in Summarization
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–26
Language:
URL:
https://www.aclweb.org/anthology/W17-4503
DOI:
10.18653/v1/W17-4503
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4503.pdf