Neural Extractive Summarization with Side Information
This repository releases codes for SideNet (Neural Extractive Summarization with Side Information). They use Tensorflow 0.10, please use scripts provided by Tensorflow to translate them to newer upgrades.
Please contact me at firstname.lastname@example.org for any question.
Please cite this paper if you use any of these:
Neural Extractive Summarization with Side Information, Shashi Narayan, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, ILCC, School of Informatics, University of Edinburgh, arXiv:1704.04530 (preprint)
Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. The gist of the document often lies in the side information of the document, such as title and image captions. These types of side information are often available for newswire articles. We propose to explore side information in the context of single document extractive summarization. We develop a framework for single-document summarization composed of a hierarchical document encoder and an attentionbased extractor with attention over side information. We evaluate our models on a large scale news dataset. We show that extractive summarization with side information consistently outperforms its counterpart (that does not use any side information), in terms on both informativeness and fluency.
The CNN and DM dataset (Hermann et al 2015) with Side Information
Dataset with sideinfo: http://kinloch.inf.ed.ac.uk/public/cnn-dm-sideinfo-data.zip
Dataset with oracle labels: http://kinloch.inf.ed.ac.uk/public/cnn-dm-sidenet-oracle.zip
Preprocessed CNN dataset used for training and testing
Preprocessed CNN dataset: http://kinloch.inf.ed.ac.uk/public/sidenet-cnn-inputs.tar.gz
CNN Original Sentence (test and validation sets): http://kinloch.inf.ed.ac.uk/public/cnn-original-sents.zip
CNN Gold Highlights (test and validation sets): http://kinloch.inf.ed.ac.uk/public/cnn-gold-highlights.zip
Live Demo: http://kinloch.inf.ed.ac.uk/sidenet.html