2017
EACL
EACL 2017
Crowd-Sourced Iterative Annotation for Narrative Summarization Corpora
Abstract
AbstractWe present an iterative annotation process for producing aligned, parallel corpora of abstractive and extractive summaries for narrative. Our approach uses a combination of trained annotators and crowd-sourcing, allowing us to elicit human-generated summaries and alignments quickly and at low cost. We use crowd-sourcing to annotate aligned phrases with the text-to-text generation techniques needed to transform each phrase into the other. We apply this process to a corpus of 476 personal narratives, which we make available on the Web.
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Interdisciplinary Bridge
- Interdisciplinary and Machine Learning and Natural Language Processing
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Trend Setter
- Digital Humanities
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Keyword Pioneer
- narrative summarization
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Hot Topic Early Bird
- parallel corpus
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Generation > Language Modeling
Natural Language Processing > Generation > Summarization
Natural Language Processing > Resources & Methods
Interdisciplinary > Linguistics > Computational Linguistics
Interdisciplinary > Science > Digital Humanities
Machine Learning > Learning Types > Supervised Learning