Exportar Publicação

Aparício, M., Figueiredo, P., Raposo, F., de Matos, D., Ribeiro, R. & Marujo, L. (2016). Summarization of films and documentaries based on subtitles and scripts. Pattern Recognition Letters. 73, 7-12

A. M. Aparício et al.,  "Summarization of films and documentaries based on subtitles and scripts", in Pattern Recognition Letters, vol. 73, pp. 7-12, 2016

@article{aparício2016_1714988853521,
	author = "Aparício, M. and Figueiredo, P. and Raposo, F. and de Matos, D. and Ribeiro, R. and Marujo, L.",
	title = "Summarization of films and documentaries based on subtitles and scripts",
	journal = "Pattern Recognition Letters",
	year = "2016",
	volume = "73",
	number = "",
	doi = "10.1016/j.patrec.2015.12.016",
	pages = "7-12",
	url = "http://www.sciencedirect.com/science/article/pii/S0167865516000039"
}

TY  - JOUR
TI  - Summarization of films and documentaries based on subtitles and scripts
T2  - Pattern Recognition Letters
VL  - 73
AU  - Aparício, M.
AU  - Figueiredo, P.
AU  - Raposo, F.
AU  - de Matos, D.
AU  - Ribeiro, R.
AU  - Marujo, L.
PY  - 2016
SP  - 7-12
SN  - 0167-8655
DO  - 10.1016/j.patrec.2015.12.016
UR  - http://www.sciencedirect.com/science/article/pii/S0167865516000039
AB  - We assess the performance of generic text summarization algorithms applied to films and documentaries, using extracts from news articles produced by reference models of extractive summarization. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries against news abstracts, plot summaries, and synopses. We show that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films. Despite the different nature of films and documentaries, their relative behavior is in accordance with that obtained for news articles.
ER  -