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A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
TLDR
A new framework for evaluating story understanding and script learning: the `Story Cloze Test’, which requires a system to choose the correct ending to a four-sentence story, and a new corpus of 50k five- Sentence commonsense stories, ROCStories, to enable this evaluation.
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Exploring Content Models for Multi-Document Summarization
TLDR
The final model, HierSum, utilizes a hierarchical LDA-style model (Blei et al., 2004) to represent content specificity as a hierarchy of topic vocabulary distributions and yields state-of-the-art ROUGE performance and in pairwise user evaluation strongly outperforms Toutanova et al. (2007)'s state of theart discriminative system.
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The Impact of Frequency on Summarization
TLDR
SumBasic is described, a summarization system that exploits frequency exclusively to create summaries and it is demonstrated how a frequency-based summarizer can incorporate context adjustment in a natural way and show that this adjustment contributes to the good performance of the summarizer and is sufficient means for duplication removal in multi-document summarization.
Visual Storytelling
TLDR
Modelling concrete description as well as figurative and social language, as provided in this dataset and the storytelling task, has the potential to move artificial intelligence from basic understandings of typical visual scenes towards more and more human-like understanding of grounded event structure and subjective expression.
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A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories
TLDR
A new framework for evaluating story understanding and script learning: the 'Story Cloze Test', which requires a system to choose the correct ending to a four-sentence story, and a new corpus of ~50k five- Sentence commonsense stories, ROCStories, to enable this evaluation.
Generating Natural Questions About an Image
TLDR
This paper introduces the novel task of Visual Question Generation, where the system is tasked with asking a natural and engaging question when shown an image, and provides three datasets which cover a variety of images from object-centric to event-centric.
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Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
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Using Contextual Speller Techniques and Language Modeling for ESL Error Correction
TLDR
A decisiontree approach inspired by contextual spelling systems for detection and correction suggestions, and a large language model trained on the Gigaword corpus to provide additional information to filter out spurious suggestions are used.
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A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization
TLDR
The research shows that a frequency based summarizer can achieve performance comparable to that of state-of-the-art systems, but only with a good composition function; context sensitivity improves performance and significantly reduces repetition.
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Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources
We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its importance in the document. We apply…
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