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Neural Abstractive Text Summarization

Abstractive text summarization is a complex task whose goal is to generate a concise version of a text without necessarily reusing the sentences from the original source, but still preserving the meaning and the key contents. We address this issue by modeling the problem as a sequence to sequence learning and exploiting Recurrent Neural Networks (RNNs). This work is a discussion about our ongoing research on abstractive text summarization, where our aim is to investigate methods to infuse prior knowledge into deep neural networks. We believe that these approaches can obtain better performance than the state-of-the-art models for generating well-formed and meaningful summaries.