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Evaluating Content Selection in Summarization: The Pyramid Method
TLDR
It is argued that the method presented is reliable, predictive and diagnostic, thus improves considerably over the shortcomings of the human evaluation method currently used in the Document Understanding Conference.
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Automatic sense prediction for implicit discourse relations in text
We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as "but" or "because".…
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Revisiting Readability: A Unified Framework for Predicting Text Quality
TLDR
This study combines lexical, syntactic, and discourse features to produce a highly predictive model of human readers' judgments of text readability and demonstrates that discourse relations are strongly associated with the perceived quality of text.
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CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset
TLDR
An audio-visual dataset uniquely suited for the study of multi-modal emotion expression and perception, which consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states, can be used to probe other questions concerning the audio- visual perception of emotion.
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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.
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Using Syntax to Disambiguate Explicit Discourse Connectives in Text
TLDR
It is demonstrated that syntactic features improve performance in both disambiguation tasks and state-of-the-art results for identifying discourse vs. non-discourse usage and human-level performance on sense disambIGuation are reported.
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A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
TLDR
A corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials is presented and a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine are outlined.
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A Survey of Text Summarization Techniques
TLDR
This chapter gives a broad overview of existing approaches based on how representation, sentence scoring or summary selection strategies alter the overall performance of the summarizer, and points out some of the peculiarities of the task of summarization.
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The Pyramid Method: Incorporating human content selection variation in summarization evaluation
TLDR
This article proposes a method for analysis of multiple human abstracts into semantic content units, which serves as the basis for an evaluation method that incorporates the observed variation and is predictive of different equally informative summaries.
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