Research Article Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization Atif Khan , 1 Qaiser Shah, 1 M. Irfan Uddin, 2 Fasee Ullah , 3 Abdullah Alharbi, 4 Hashem Alyami, 5 and Muhammad Adnan Gul 1 1 Department of Computer Science, Islamia College Peshawar, Peshawar, KP, Pakistan 2 Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan 3 Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China 4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia 5 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia Correspondence should be addressed to Atif Khan; atifkhan@icp.edu.pk and Fasee Ullah; faseekhan@gmail.com Received 1 July 2020; Accepted 30 July 2020; Published 25 August 2020 Guest Editor: Furqan Aziz Copyright © 2020 Atif Khan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. e discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. us, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. e proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/ sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure. 1. Introduction e content shared by Internet users in online forum platforms is a valuable repository of information. Since information and communication technologies (ICT) are rising at a high pace, a bulk of data is available online. Many users use web services to share their knowledge about specific subjects, which exist on the web in the form of discussion forum, blogs, or any other user generated content [1]. Discussion forums are also known as web forum, message boards, and bulletin boards. In the current era, discussion forums are becoming very popular because these platforms give easy access to users to share their information and allow them to discuss issues/topics of common interest. Huge data on the web come from discussion forums, which contain millions of threads. ese threads are a valuable source of knowledge for Internet users as they have infor- mation about various topics. e threads, also called dis- cussion threads, are important for those who post as well as for “lurkers,” users who only read the replies. e discussion threads pertaining to single topic comprise a huge number of individual posts, which makes it hard for forum users to determine the most significant information in the thread. Hindawi Complexity Volume 2020, Article ID 4750871, 11 pages https://doi.org/10.1155/2020/4750871