@inproceedings{cucchiarelli-etal-2017-write,
title = "What to Write? A topic recommender for journalists",
author = "Cucchiarelli, Alessandro and
Morbidoni, Christian and
Stilo, Giovanni and
Velardi, Paola",
editor = "Popescu, Octavian and
Strapparava, Carlo",
booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4204",
doi = "10.18653/v1/W17-4204",
pages = "19--24",
abstract = "In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers{'} communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter andWikipedia, either not covered or poorly covered in the published news articles.",
}
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%0 Conference Proceedings
%T What to Write? A topic recommender for journalists
%A Cucchiarelli, Alessandro
%A Morbidoni, Christian
%A Stilo, Giovanni
%A Velardi, Paola
%Y Popescu, Octavian
%Y Strapparava, Carlo
%S Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F cucchiarelli-etal-2017-write
%X In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter andWikipedia, either not covered or poorly covered in the published news articles.
%R 10.18653/v1/W17-4204
%U https://aclanthology.org/W17-4204
%U https://doi.org/10.18653/v1/W17-4204
%P 19-24
Markdown (Informal)
[What to Write? A topic recommender for journalists](https://aclanthology.org/W17-4204) (Cucchiarelli et al., 2017)
ACL
- Alessandro Cucchiarelli, Christian Morbidoni, Giovanni Stilo, and Paola Velardi. 2017. What to Write? A topic recommender for journalists. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 19–24, Copenhagen, Denmark. Association for Computational Linguistics.