Category-Driven Content Selection

Rania Mohammed; Laura Perez-Beltrachini; Claire Gardent

doi:10.18653/v1/W16-6616

Category-Driven Content Selection

Rania Mohammed, Laura Perez-Beltrachini, Claire Gardent

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we introduce a content selection method where the communicative goal is to describe entities of different categories (e.g., astronauts, universities or monuments). We argue that this method provides an interesting basis both for generating descriptions of entities and for semi-automatically constructing a
benchmark on which to train, test and compare data-to-text generation systems.

Original language	English
Title of host publication	Proceedings of The 9th International Natural Language Generation conference
Publisher	Association for Computational Linguistics
Pages	94-98
Number of pages	5
DOIs	https://doi.org/10.18653/v1/W16-6616
Publication status	Published - 8 Sep 2016
Event	9th International Natural Language Generation conference - Edinburgh, United Kingdom Duration: 5 Sep 2016 → 8 Sep 2016 http://www.macs.hw.ac.uk/InteractionLab/INLG2016/

Conference

Conference	9th International Natural Language Generation conference
Abbreviated title	INLG 2016
Country/Territory	United Kingdom
City	Edinburgh
Period	5/09/16 → 8/09/16
Internet address	http://www.macs.hw.ac.uk/InteractionLab/INLG2016/

Access to Document

10.18653/v1/W16-6616Licence: Creative Commons: Attribution (CC-BY)

W16-6616Final published version, 116 KBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/W16-6616/

Cite this

@inproceedings{310eba5220cc43cd82499e9ba08acd4b,

title = "Category-Driven Content Selection",

abstract = "In this paper, we introduce a content selection method where the communicative goal is to describe entities of different categories (e.g., astronauts, universities or monuments). We argue that this method provides an interesting basis both for generating descriptions of entities and for semi-automatically constructing abenchmark on which to train, test and compare data-to-text generation systems.",

author = "Rania Mohammed and Laura Perez-Beltrachini and Claire Gardent",

year = "2016",

month = sep,

day = "8",

doi = "10.18653/v1/W16-6616",

language = "English",

pages = "94--98",

booktitle = "Proceedings of The 9th International Natural Language Generation conference",

publisher = "Association for Computational Linguistics",

note = "9th International Natural Language Generation conference , INLG 2016 ; Conference date: 05-09-2016 Through 08-09-2016",

url = "http://www.macs.hw.ac.uk/InteractionLab/INLG2016/",

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T1 - Category-Driven Content Selection

AU - Mohammed, Rania

AU - Perez-Beltrachini, Laura

AU - Gardent, Claire

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N2 - In this paper, we introduce a content selection method where the communicative goal is to describe entities of different categories (e.g., astronauts, universities or monuments). We argue that this method provides an interesting basis both for generating descriptions of entities and for semi-automatically constructing abenchmark on which to train, test and compare data-to-text generation systems.

AB - In this paper, we introduce a content selection method where the communicative goal is to describe entities of different categories (e.g., astronauts, universities or monuments). We argue that this method provides an interesting basis both for generating descriptions of entities and for semi-automatically constructing abenchmark on which to train, test and compare data-to-text generation systems.

U2 - 10.18653/v1/W16-6616

DO - 10.18653/v1/W16-6616

M3 - Conference contribution

SP - 94

EP - 98

BT - Proceedings of The 9th International Natural Language Generation conference

PB - Association for Computational Linguistics

T2 - 9th International Natural Language Generation conference

Y2 - 5 September 2016 through 8 September 2016

ER -