Abstract
The learners and teachers of the teaching-learning process highly depend on online learning systems such as E-learning, which contains huge volumes of electronic contents related to a course. The multi-document summarization (MDS) is useful for summarizing such electronic contents. This article applies the task of MDS in an E-learning context. The objective of this article is threefold: 1) design a generic graph based multi-document summarizer DSGA (Dynamic Summary Generation Algorithm) to produce a variable length (dynamic) summary of academic text based learning materials based on a learner's request; 2) analyze the summary generation process; 3) perform content-based and task-based evaluations on the generated summary. The experimental results show that the DSGA summarizer performs better than the graph-based summarizers LexRank (LR) and Aggregate Similarity (AS). From the task-based evaluation, it is observed that the generated summary helps the learners to understand and comprehend the materials easily.
Highlights
To access relevant information quickly in today’s vast amount of online information, the automatic text summarization (ATS) is an important and timely too1
The learners and teachers of the teaching-learning process highly depend on online learning systems such as E-learning, which contains huge volumes of electronic contents related to a course
From Table 1and Figures 7, 8, and 9, it is clearly understood that the ROUGE-1, ROUGE-2, and ROUGE-L recalls of the Dynamic summary generation algorithm (DSGA) summary is greater than or equal to the ROUGE-1, ROUGE-2, and ROUGE-L recalls of the LR and Aggregate similarity (AS) summaries in most of the cases
Summary
To access relevant information quickly in today’s vast amount of online information, the automatic text summarization (ATS) is an important and timely too. This article generates a summary of learning materials using the MDS with sentence similarity graphs. It uses the graph structure, maximal clique to provide a concept oriented summary (Tomita et al, 2011). This article covers all important concepts of the given text based learning materials by selecting summary sentences from a diverse. The earlier approaches for MDS are statistical, linguistic, and feature based (Ferreira et al, 2014), centroid based (Rossiello et al, 2017), clustering (Cai & Li, 2013; Fejer & Omar, 2015), machine learning (Cao et al, 2017), etc. This article utilizes group relations using the sentence similarity graphs of the input documents with maximal cliques
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Web-Based Learning and Teaching Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
