Naslov
Extractive Text Summarization Based on Selectivity Ranking

Autori
Aljević, Dino ; Todorovski, Ljupčo ; Martinčić-Ipšić, Sanda

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo

Izvornik
2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) / - Lahti : IEEE INISTA, 2021, 1-6

ISBN
978-1-6654-3602-1

Skup
2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

Mjesto i datum
Kocaeli, Turska, 25.08.2021. - 27.08.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
natural language processing, extractive summarization, graph-based method, selectivity measure

Sažetak
Extractive summarization of text documents deals with automatic creation of a summary by combining the most salient sentences extracted from the original text document into a more concise form. In this paper, we introduce a novel graphbased method for extractive summarization that transforms a given text into a graph of interconnected sentences and employs computationally efficient selectivity measure to measure the importance of graph nodes. In turn, the text summary is build upon the sentences corresponding to the most important graph nodes. The edges in the graph are based on three measures of similarity between sentences, i.e., Mihalcea’s, Jaccard and Cosine. The first among the three leads to best performance, which also compares favorably to the performance of three alternative summarization methods of TF-IDF, centroid and TextRank. The results confirm that selectivity of nodes in a text graph build using the Mihalcea’s similarity between sentences is a computationally efficient and well performing method for extractive summarization

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti

POVEZANOST RADA