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In this paper, we introduce Iterative Text Summarization (ITS), aniteration-based model for supervised extractive text summarization, inspired bythe observation that it is often necessary for a human to read an articlemultiple times in order to fully understand and summarize its contents. Currentsummarization approaches read through a document only once to generate adocument representation, resulting in a sub-optimal representation. To addressthis issue we introduce a model which iteratively polishes the documentrepresentation on many passes through the document. As part of our model, wealso introduce a selective reading mechanism that decides more accurately theextent to which each sentence in the model should be updated. Experimentalresults on the CNN/DailyMail and DUC2002 datasets demonstrate that our modelsignificantly outperforms state-of-the-art extractive systems when evaluated bymachines and by humans.

Iterative Document Representation Learning Towards Summarization with Polishing