Modelling, visualising and summarising documents with a single convolutional neural network

Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Process-ing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nu-anced semantics. Our model is based on an extended Dynamic Convolution Neu-ral Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no fea-ture engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel vi-sualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts.