Hybrid MemNet for Extractive Summarization
Abstract
Extractive text summarization has been an extensive research problem in the field of natural language understanding. While the conventional approaches rely mostly on manually compiled features to generate the summary, few attempts have been made in developing data-driven systems for extractive summarization. To this end, we present a fully data-driven end-to-end deep network which we call as Hybrid MemNet for single document summarization task. The network learns the continuous unified representation of a document before generating its summary. It jointly captures local and global sentential information along with the notion of summary worthy sentences. Experimental results on two different corpora confirm that our model shows significant performance gains compared with the state-of-the-art baselines.
Cite
@article{arxiv.1912.11701,
title = {Hybrid MemNet for Extractive Summarization},
author = {Abhishek Kumar Singh and Manish Gupta and Vasudeva Varma},
journal= {arXiv preprint arXiv:1912.11701},
year = {2019}
}
Comments
Accepted in CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management