Standard
Exploring clustering for multi-document Arabic summarisation. /
El-Haj, Mahmoud; Kruschwitz, Udo; Fox, Chris.
Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings. ed. / Mohamed Vall Mohamed Salem; Khaled Shaalan; Farhad Oroumchian; Azadeh Shakery; Halim Khelalfa. Berlin: Springer, 2011. p. 550-561 (Lecture Notes in Computer Science; Vol. 7097).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
El-Haj, M, Kruschwitz, U & Fox, C 2011,
Exploring clustering for multi-document Arabic summarisation. in MVM Salem, K Shaalan, F Oroumchian, A Shakery & H Khelalfa (eds),
Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings. Lecture Notes in Computer Science, vol. 7097, Springer, Berlin, pp. 550-561.
https://doi.org/10.1007/978-3-642-25631-8_50
APA
El-Haj, M., Kruschwitz, U., & Fox, C. (2011).
Exploring clustering for multi-document Arabic summarisation. In M. V. M. Salem, K. Shaalan, F. Oroumchian, A. Shakery, & H. Khelalfa (Eds.),
Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings (pp. 550-561). (Lecture Notes in Computer Science; Vol. 7097). Springer.
https://doi.org/10.1007/978-3-642-25631-8_50
Vancouver
El-Haj M, Kruschwitz U, Fox C.
Exploring clustering for multi-document Arabic summarisation. In Salem MVM, Shaalan K, Oroumchian F, Shakery A, Khelalfa H, editors, Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings. Berlin: Springer. 2011. p. 550-561. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-25631-8_50
Author
El-Haj, Mahmoud ; Kruschwitz, Udo ; Fox, Chris. /
Exploring clustering for multi-document Arabic summarisation. Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings. editor / Mohamed Vall Mohamed Salem ; Khaled Shaalan ; Farhad Oroumchian ; Azadeh Shakery ; Halim Khelalfa. Berlin : Springer, 2011. pp. 550-561 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{d8450bd631f444b2a6a6912a85f9ae4e,
title = "Exploring clustering for multi-document Arabic summarisation",
abstract = "In this paper we explore clustering for multi-document Arabic summarisation. For our evaluation we use an Arabic version of the DUC-2002 dataset that we previously generated using Google Translate. We explore how clustering (at the sentence level) can be applied to multi-document summarisation as well as for redundancy elimination within this process. We use different parameter settings including the cluster size and the selection model applied in the extractive summarisation process. The automatically generated summaries are evaluated using the ROUGE metric, as well as precision and recall. The results we achieve are compared with the top five systems in the DUC-2002 multi-document summarisation task.",
author = "Mahmoud El-Haj and Udo Kruschwitz and Chris Fox",
year = "2011",
doi = "10.1007/978-3-642-25631-8_50",
language = "English",
isbn = "9783642256301",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "550--561",
editor = "Salem, {Mohamed Vall Mohamed} and Khaled Shaalan and Farhad Oroumchian and Azadeh Shakery and Halim Khelalfa",
booktitle = "Information Retrieval Technology",
}
RIS
TY - GEN
T1 - Exploring clustering for multi-document Arabic summarisation
AU - El-Haj, Mahmoud
AU - Kruschwitz, Udo
AU - Fox, Chris
PY - 2011
Y1 - 2011
N2 - In this paper we explore clustering for multi-document Arabic summarisation. For our evaluation we use an Arabic version of the DUC-2002 dataset that we previously generated using Google Translate. We explore how clustering (at the sentence level) can be applied to multi-document summarisation as well as for redundancy elimination within this process. We use different parameter settings including the cluster size and the selection model applied in the extractive summarisation process. The automatically generated summaries are evaluated using the ROUGE metric, as well as precision and recall. The results we achieve are compared with the top five systems in the DUC-2002 multi-document summarisation task.
AB - In this paper we explore clustering for multi-document Arabic summarisation. For our evaluation we use an Arabic version of the DUC-2002 dataset that we previously generated using Google Translate. We explore how clustering (at the sentence level) can be applied to multi-document summarisation as well as for redundancy elimination within this process. We use different parameter settings including the cluster size and the selection model applied in the extractive summarisation process. The automatically generated summaries are evaluated using the ROUGE metric, as well as precision and recall. The results we achieve are compared with the top five systems in the DUC-2002 multi-document summarisation task.
U2 - 10.1007/978-3-642-25631-8_50
DO - 10.1007/978-3-642-25631-8_50
M3 - Conference contribution/Paper
SN - 9783642256301
T3 - Lecture Notes in Computer Science
SP - 550
EP - 561
BT - Information Retrieval Technology
A2 - Salem, Mohamed Vall Mohamed
A2 - Shaalan, Khaled
A2 - Oroumchian, Farhad
A2 - Shakery, Azadeh
A2 - Khelalfa, Halim
PB - Springer
CY - Berlin
ER -
