Dependency tree based sentence compression
Katja Filippova,Michael Strube +1 more
- pp 25-32
TLDR
A novel unsupervised method for sentence compression which relies on a dependency tree representation and shortens sentences by removing subtrees and it is demonstrated that the choice of the parser affects the performance of the system.Abstract:
We present a novel unsupervised method for sentence compression which relies on a dependency tree representation and shortens sentences by removing subtrees. An automatic evaluation shows that our method obtains result comparable or superior to the state of the art. We demonstrate that the choice of the parser affects the performance of the system. We also apply the method to German and report the results of an evaluation with humans.read more
Citations
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Optimizing Statistical Machine Translation for Text Simplification
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A Monolingual Tree-based Translation Model for Sentence Simplification
TL;DR: A Tree-based Simplification Model (TSM) is proposed, which, to the knowledge, is the first statistical simplification model covering splitting, dropping, reordering and substitution integrally.
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Problems in Current Text Simplification Research: New Data Can Help
TL;DR: This opinion paper argues that focusing on Wikipedia limits simplification research, and introduces a new simplification dataset that is a significant improvement over Simple Wikipedia, and presents a novel quantitative-comparative approach to study the quality of simplification data resources.
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Multi-Sentence Compression: Finding Shortest Paths in Word Graphs
TL;DR: Despite its simplicity, the proposed multi-sentence compression method is capable of generating grammatical and informative summaries as its experiments with English and Spanish data demonstrate.
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Controlling Output Length in Neural Encoder-Decoders
TL;DR: This paper proposed methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based and two learning-based methods, which have the capability to control length without degrading summary quality in a summarization task.
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