On-Device Extractive Text Summarization

Dhaliwal, Mehak Preet; Kumar, Rishabh; Rungta, Mukund; Tiwari, Hemant; Vala, Vanraj

doi:10.1109/icsc50631.2021.00065

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…is yet to be examined. (Dhaliwal et al, 2021). So far, real time on-device summarization is an un-chartered territory and we envisage rapid developments in this direction.…”

Section: Comparison Of Document Summarization Applicationsmentioning

confidence: 99%

Investigating Entropy for Extractive Document Summarization

Khurana,

Bhatnagar

2021

Preprint

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Automatic text summarization aims to cut down readers' time and cognitive effort by reducing the content of a text document without compromising on its essence. Ergo, informativeness is the prime attribute of document summary generated by an algorithm, and selecting sentences that capture the essence of a document is the primary goal of extractive document summarization.In this paper, we employ Shannon's entropy to capture informativeness of sentences. We employ Non-negative Matrix Factorization (NMF) to reveal probability distributions for computing entropy of terms, topics, and sentences in latent space. We present an information theoretic interpretation of the computed entropy, which is the bedrock of the proposed E-Summ algorithm, an unsupervised method for extractive document summarization. The algorithm systematically applies information theoretic principle for selecting informative sentences from important topics in the document. The proposed algorithm is generic and fast, and hence amenable to use for summarization of documents in real time. Furthermore, it is domain-, collection-independent and agnostic to the language of the document. Benefiting from strictly positive NMF factor matrices, E-Summ algorithm is transparent and explainable too.We use standard ROUGE toolkit for performance evaluation of the proposed method on four well known public data-sets. We also perform quantitative as- *

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“…is yet to be examined. (Dhaliwal et al, 2021). So far, real time on-device summarization is an un-chartered territory and we envisage rapid developments in this direction.…”

Section: Comparison Of Document Summarization Applicationsmentioning

confidence: 99%

Investigating Entropy for Extractive Document Summarization

Khurana,

Bhatnagar

2021

Preprint

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“…The authors used a LSTM-NN (neural network) for the sentence selection based on semantic features, synthetic features and ensembled features. Compared the LSTM-NN model with the baseline models of hierarchical attention-based bidirectional gated recurrent unit (Bi-GRU), CNN, Bi-GRU and the newer state-of-the-art models SummaRuNNer [96], BanditSum [97]. The LSTM-NN model outperformed all other four models.…”

Section: ) Text Summarizationmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

Tao

et al. 2022

IEEE Access

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Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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