Distraction-Based Neural Networks for Document Summarization

Abstract

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e.g., documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.

Published in IJCAI-2016: the 25th International Joint Conference on Artificial Intelligence

References in corpus (4)

• ADADELTA: An Adaptive Learning Rate Method
• Natural Language Processing (almost) from Scratch
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• A Hierarchical Neural Autoencoder for Paragraphs and Documents

Cited by in corpus (5)

• Deep Recurrent Generative Decoder for Abstractive Text Summarization
• Scoring Sentence Singletons and Pairs for Abstractive Summarization
• Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
• Guiding Extractive Summarization with Question-Answering Rewards
• Ranking sentences from product description & bullets for better search