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We propose an end-to-end neural model for zero-shot abstractive textsummarization of paragraphs, and introduce a benchmark task, ROCSumm, based onROCStories, a subset for which we collected human summaries. In this task,five-sentence stories (paragraphs) are summarized with one sentence, usinghuman summaries only for evaluation. We show results for extractive and humanbaselines to demonstrate a large abstractive gap in performance. Our model,SummAE, consists of a denoising auto-encoder that embeds sentences andparagraphs in a common space, from which either can be decoded. Summaries forparagraphs are generated by decoding a sentence from the paragraphrepresentations. We find that traditional sequence-to-sequence auto-encodersfail to produce good summaries and describe how specific architectural choicesand pre-training techniques can significantly improve performance,outperforming extractive baselines. The data, training, evaluation code, andbest model weights are open-sourced.

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders