@InProceedings{isonuma-EtAl:2017:EMNLP2017,
  author    = {Isonuma, Masaru  and  Fujino, Toru  and  Mori, Junichiro  and  Matsuo, Yutaka  and  Sakata, Ichiro},
  title     = {Extractive Summarization Using Multi-Task Learning with Document Classification},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2101--2110},
  abstract  = {The need for automatic document summarization that can be used for practical
	applications is increasing rapidly. In this paper, we propose a general
	framework for summarization that extracts sentences from a document using
	externally related information. Our work is aimed at single document
	summarization using small amounts of reference summaries. In particular, we
	address document summarization in the framework of multi-task learning using
	curriculum learning for sentence extraction and document classification. The
	proposed framework enables us to obtain better feature representations to
	extract sentences from documents. We evaluate our proposed summarization method
	on two datasets: financial report and news corpus. Experimental results
	demonstrate that our summarizers achieve performance that is comparable to
	state-of-the-art systems.},
  url       = {https://www.aclweb.org/anthology/D17-1223}
}