TY - GEN
T1 - Graph ranking on maximal frequent sequences for single extractive text summarization
AU - Ledeneva, Yulia
AU - García-Hernández, René Arnulfo
AU - Gelbukh, Alexander
PY - 2014
Y1 - 2014
N2 - We suggest a new method for the task of extractive text summarization using graph-based ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graph-based algorithm. We show that the proposed method produces results superior to the-state-of-the-art methods; in addition, the best sentences were found with this method. We prove that MFS are better than other terms. Moreover, we show that the longer is MFS, the better are the results. If the stop-words are excluded, we lose the sense of MFS, and the results are worse. Other important aspect of this method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, and languages.
AB - We suggest a new method for the task of extractive text summarization using graph-based ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graph-based algorithm. We show that the proposed method produces results superior to the-state-of-the-art methods; in addition, the best sentences were found with this method. We prove that MFS are better than other terms. Moreover, we show that the longer is MFS, the better are the results. If the stop-words are excluded, we lose the sense of MFS, and the results are worse. Other important aspect of this method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, and languages.
UR - http://www.scopus.com/inward/record.url?scp=84958537389&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-54903-8_39
DO - 10.1007/978-3-642-54903-8_39
M3 - Contribución a la conferencia
SN - 9783642549021
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 466
EP - 480
BT - Computational Linguistics and Intelligent Text Processing - 15th International Conference, CICLing 2014, Proceedings
PB - Springer Verlag
T2 - 15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014
Y2 - 6 April 2014 through 12 April 2014
ER -