Traditional approaches to extractive summarization rely heavily on humanengineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor. This architecture allows us to develop different classes of summarization models which can extract sentences or words. We train our models on large scale corpora containing hundreds of thousands of document-summary pairs 1 . Experimental results on two summarization datasets demonstrate that our models obtain results comparable to the state of the art without any access to linguistic annotation.
Another extractive summarization model was proposed by Cheng and Lapata REF in which they have treated the single document summarization as a sequence labeling task using a document encoder and attention-based extractor.
Neural Summarization by Extracting Sentences and Words
ArXiv
ArXiv
Computer Science