1,626 research outputs found
Deep Recurrent Generative Decoder for Abstractive Text Summarization
We propose a new framework for abstractive text summarization based on a
sequence-to-sequence oriented encoder-decoder model equipped with a deep
recurrent generative decoder (DRGN).
Latent structure information implied in the target summaries is learned based
on a recurrent latent random model for improving the summarization quality.
Neural variational inference is employed to address the intractable posterior
inference for the recurrent latent variables.
Abstractive summaries are generated based on both the generative latent
variables and the discriminative deterministic states.
Extensive experiments on some benchmark datasets in different languages show
that DRGN achieves improvements over the state-of-the-art methods.Comment: 10 pages, EMNLP 201
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Keyphrase extraction from documents is useful to a variety of applications
such as information retrieval and document summarization. This paper presents
an end-to-end method called DivGraphPointer for extracting a set of diversified
keyphrases from a document. DivGraphPointer combines the advantages of
traditional graph-based ranking methods and recent neural network-based
approaches. Specifically, given a document, a word graph is constructed from
the document based on word proximity and is encoded with graph convolutional
networks, which effectively capture document-level word salience by modeling
long-range dependency between words in the document and aggregating multiple
appearances of identical words into one node. Furthermore, we propose a
diversified point network to generate a set of diverse keyphrases out of the
word graph in the decoding process. Experimental results on five benchmark data
sets show that our proposed method significantly outperforms the existing
state-of-the-art approaches.Comment: Accepted to SIGIR 201
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