Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization
المؤلفون المشاركون
Ullah, Fasee
Gul, Muhammad Adnan
Khan, Atif
Alyami, Hashem
Uddin, M. Irfan
ALharbi, Abdullah
Shah, Qaiser
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-25
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
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.
The 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.
Thus, 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.
The 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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Khan, Atif& Shah, Qaiser& Uddin, M. Irfan& Ullah, Fasee& ALharbi, Abdullah& Alyami, Hashem…[et al.]. 2020. Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1142062
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Khan, Atif…[et al.]. Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1142062
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Khan, Atif& Shah, Qaiser& Uddin, M. Irfan& Ullah, Fasee& ALharbi, Abdullah& Alyami, Hashem…[et al.]. Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1142062
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142062
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر