Extraction of Salient Sentences from Labelled Documents

Abstract

We present a hierarchical convolutional document model with an architecture designed to support introspection of the document structure. Using this model, we show how to use visualisation techniques from the computer vision literature to identify and extract topic-relevant sentences. We also introduce a new scalable evaluation technique for automatic sentence extraction systems that avoids the need for time consuming human annotation of validation data.

arXiv admin note: substantial text overlap with arXiv:1406.3830

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Natural Language Processing (almost) from Scratch
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• Deep Learning for Answer Sentence Selection
• Question Answering with Subgraph Embeddings

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• Evaluating Saliency Methods for Neural Language Models
• Evaluating Explanation Methods for Neural Machine Translation
• Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability
• Attribution Analysis of Grammatical Dependencies in LSTMs
• On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
• Distilling neural networks into skipgram-level decision lists