135 research outputs found
Neural Summarization by Extracting Sentences and Words
Traditional approaches to extractive summarization rely heavily on
human-engineered 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. 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.Comment: ACL2016 conference paper with appendi
Hierarchically-Attentive RNN for Album Summarization and Storytelling
We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album
photos, select representative (summary) photos, and compose the story.
Automatic and human evaluations show our model achieves better performance on
selection, generation, and retrieval than baselines.Comment: To appear at EMNLP-2017 (7 pages
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