Title: Extracting useful reply-posts for text forum threads summarisation using quality features and classification methods

Authors: Akram Osman; Naomie Salim

Addresses: Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia ' Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

Abstract: Text forums threads have a large amount of information furnished by users who discuss on a specific topic. At times, certain thread reply-posts are entirely off-topic, thereby deviating from the main discussion. It negatively affects the user's preference to continue replying to the discussion. Thus, there is a possibility that the user prefers to read certain selected reply-posts that provide a short summary of the topic of the discussion. The objective of the paper is to choose quality reply-posts regarding a topic considered in the initial-post, which also serve a brief summary. We offer an exhaustive examination of the conversational patterns of the threads on the basis of 12 quality features for analysis. These features can ensure selection of relevant reply-posts for the thread summary. Experimental outcomes obtained using two datasets show that the presented techniques considerably enhanced the performance in selecting initial-post replies pairs for text forum threads summarisation.

Keywords: information retrieval; initial-post replies pairs; text data; text forum threads; TFThs; text forum threads summarisation; text summarisation; thread retrieval.

DOI: 10.1504/IJDMMM.2020.108725

International Journal of Data Mining, Modelling and Management, 2020 Vol.12 No.3, pp.330 - 349

Received: 08 Oct 2018
Accepted: 16 May 2019
Published online: 29 Jul 2020 *

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