Extractive Summarization of Documents by Combining Semantic Content and Non-Structured Features

摘要

Current extractive summarization models utilize semantic content and non-structured features of sentences respectively to identify the sentence importance. In this paper, we present a new approach to extractive summarization by combining semantic content and non-structured features of sentences based on convolutional neural network and recurrent neural network, called CRSum. In this model, firstly, semantic content of sentences are learned by convolutional neural network, and non-structured features of sentences are learned by recurrent neural network. Secondly, we investigate whether a sentence can be used as the summary according to the above knowledge we learned. What's more, all the predictions of CRSum model can be interpreted by visualizing semantic content and non-structured features of sentences. Experimental results on LSCTC and CNN/Daily Mail corpus show that its performance is better than that of the baseline systems and surpass the state-of-the-art model in Rouge-L.