Title: Graph-based cumulative score using statistical features for multilingual automatic text summarisation
Authors: Abdelkrime Aries; Djamel Eddine Zegour; Walid Khaled Hidouci
Addresses: École Nationale Supérieure d'Informatique (ESI, ex. INI), Algiers, Algeria ' École Nationale Supérieure d'Informatique (ESI, ex. INI), Algiers, Algeria ' École Nationale Supérieure d'Informatique (ESI, ex. INI), Algiers, Algeria
Abstract: Multilingual summarisation began to receive more attention these late years. Many approaches can be used to achieving this, among them: statistical and graph-based approaches. Our idea is to combine these two approaches into a new extractive text summarisation method. Surface statistical features are used to calculate a primary score for each sentence. The graph is used to selecting some candidate sentences and calculating a final score for each sentence based on its primary score and those of its neighbours in the graph. We propose four variants to calculating the cumulative score of a sentence. Also, the order of sentences is an important aspect of summary readability. We propose some other algorithms to generating the summary not just based on final scores but on sentences connections in the graph. The method is tested using MultiLing'15 workshop's MSS corpus and ROUGE metric. It is evaluated against some well known methods and it gives promising results.
Keywords: automatic text summarisation; ATS; graph-based summarisation; statistical features; multilingual summarisation; extractive summarisation.
International Journal of Data Mining, Modelling and Management, 2021 Vol.13 No.1/2, pp.37 - 64
Received: 04 Jul 2018
Accepted: 28 Mar 2019
Published online: 09 Feb 2021 *