Multi-document Text Summarization in E-learning System for Operating System Domain

Saraswathi, S.; Hemamalini, M.; Janani, S.; Priyadharshini, V.

doi:10.1007/978-3-642-22726-4_19

Cited by 7 publications

(10 citation statements)

References 5 publications

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“…In [17]- [20] summarization techniques have been applied in the learning context. The work presented by [20] focused on automatically answering specific learners' questions.…”

Section: Related Workmentioning

confidence: 99%

Recommending Personalized Summaries of Teaching Materials

2019

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Teaching activities have nowadays been supported by a variety of electronic devices. Formative assessment tools allow teachers to evaluate the level of understanding of learners during frontal lessons and to tailor the next teaching activities accordingly. Despite plenty of teaching materials are available in the textual form, manually exploring these very large collections of documents can be extremely time-consuming. The analysis of learner-produced data (e.g., test outcomes) can be exploited to recommend short extracts of teaching documents based on the actual learner's needs. This paper proposes a new methodology to recommend summaries of potentially large teaching documents. Summary recommendations are customized to student's needs according to the results of comprehension tests performed at the end of frontal lectures. Specifically, students undergo multiple-choice tests through a mobile application. In parallel, a set of topic-specific summaries of the teaching documents is generated. They consist of the most significant sentences related to a specific topic. According to the results of the tests, summaries are personally recommended to students. We assessed the applicability of the proposed approach in real context, i.e., a B.S. university-level course. The results achieved in the experimental evaluation confirmed its usability.

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“…In [17]- [20] summarization techniques have been applied in the learning context. The work presented by [20] focused on automatically answering specific learners' questions.…”

Section: Related Workmentioning

confidence: 99%

Recommending Personalized Summaries of Teaching Materials

2019

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“…[22], [23]), and (c) the extraction of salient content from large teaching data and its exploitation through online or mobile platforms (e.g. [24], [25]). The system proposed in this paper falls into category (c).…”

Section: Related Workmentioning

confidence: 99%

“…The system proposed in this paper falls into category (c). The tool proposed in [25] focuses on automatically answering to learners' questions by applying text summarization techniques, while in [24] summaries of textual documents are generated to improve the accessibility of the learning materials through mobile devices. Unlike [24], [25], the approach proposed in this paper is not query-driven and relies on text classification techniques rather than on summarization algorithms.…”

Section: Related Workmentioning

confidence: 99%

Highlighter: Automatic Highlighting of Electronic Learning Documents

Baralis

Cagliero

2018

IEEE Trans. Emerg. Topics Comput.

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Electronic textual documents are among the most popular teaching content accessible through e-learning platforms. Teachers or learners with different levels of knowledge can access the platform and highlight portions of textual content which are deemed as particularly relevant. The highlighted documents can be shared with the learning community in support of oral lessons or individual learning. However, highlights are often incomplete or unsuitable for learners with different levels of knowledge. This paper addresses the problem of predicting new highlights of partly highlighted electronic learning documents. With the goal of enriching teaching content with additional features, text classification techniques are exploited to automatically analyze portions of documents enriched with manual highlights made by users with different levels of knowledge and to generate ad hoc prediction models. Then, the generated models are applied to the remaining content to suggest highlights. To improve the quality of the learning experience, learners may explore highlights generated by models tailored to different levels of knowledge. We tested the prediction system on real and benchmark documents highlighted by domain experts and we compared the performance of various classifiers in generating highlights. The achieved results demonstrated the high accuracy of the predictions and the applicability of the proposed approach to real teaching documents.

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“…In this field, several efforts have been made to design and develop text mining solutions aimed to analyze digital learning documents (e.g. [4], [5], [6], [7], [8], [9]). Some of them (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Some of them (e.g. [4], [7], [8], [9]) addressed the problem of summarizing learning materials to ease document exploration or to improve content accessibility through portable devices with limited bandwidth or resolution. The importance of text summaries in learning activities has been confirmed by previous studies [10].…”

Section: Introductionmentioning

confidence: 99%

Test-Driven Summarization: Combining Formative Assessment with Teaching Document Summarization

Cagliero

Farinetti

Baralis³

2017

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)

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The diffusion of learning technologies has fostered the use of mobile and Web-based applications to assess the knowledge level of learners. In parallel, an increasing research interest has been devoted to studying new learning analytics tools able to summarize the content of large sets of learning documents. To bridge the gap between formative assessment tools and document summarization systems, this paper addresses the problem of recommending short summaries of large sets of learning documents based on the outcomes of multiplechoice tests. Specifically, it presents a new methodology for integrating formative assessment through mobile applications and summarization of learning documents in textual form. The content of the multiple-choice tests is exploited to drive the generation of document summaries tailored to specific topics. Furthermore, the outcomes of the tests are used to automatically recommend the generated summaries to learners based on their actual needs. As a case study, we performed an evaluation experience of students' progresses, which was conducted in the context of a university-level course. The achieved results show the applicability of the proposed methodology.

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Multi-document Text Summarization in E-learning System for Operating System Domain

Cited by 7 publications

References 5 publications

Recommending Personalized Summaries of Teaching Materials

Recommending Personalized Summaries of Teaching Materials

Highlighter: Automatic Highlighting of Electronic Learning Documents

Test-Driven Summarization: Combining Formative Assessment with Teaching Document Summarization

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