@inproceedings{ponza-etal-2018-facts,
title = "Facts That Matter",
author = "Ponza, Marco and
Del Corro, Luciano and
Weikum, Gerhard",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1129",
doi = "10.18653/v1/D18-1129",
pages = "1043--1048",
abstract = "This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.",
}
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<abstract>This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.</abstract>
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%0 Conference Proceedings
%T Facts That Matter
%A Ponza, Marco
%A Del Corro, Luciano
%A Weikum, Gerhard
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ponza-etal-2018-facts
%X This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.
%R 10.18653/v1/D18-1129
%U https://aclanthology.org/D18-1129
%U https://doi.org/10.18653/v1/D18-1129
%P 1043-1048
Markdown (Informal)
[Facts That Matter](https://aclanthology.org/D18-1129) (Ponza et al., EMNLP 2018)
ACL
- Marco Ponza, Luciano Del Corro, and Gerhard Weikum. 2018. Facts That Matter. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1043–1048, Brussels, Belgium. Association for Computational Linguistics.