Document vector embedding based extractive text summarization system for Hindi and English text

Rani, Ruby; Lobiyal, D. K.

doi:10.1007/s10489-021-02871-9

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…Extractive methods involve extracting words and sentences and other semantic units from the original text. Representative extractive methods include semantic-information-based methods [8] and structural-information-based methods [9,10]. Abstractive summarization methods are closer to manual summarization, which restates the original text with words, sentences, and phrases that are different from the original text.…”

Section: Related Workmentioning

confidence: 99%

An Abstract Summarization Method Combining Global Topics

Duan

et al. 2022

Applied Sciences

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Existing abstractive summarization methods only focus on the correlation between the original words and the summary words, ignoring the topics’ influence on the summaries. To this end, an abstract summarization method combining global topic information, ACGT, is proposed. A topic information extractor, based on Latent Dirichlet Allocation, is constructed to extract key topic information from the original text, and an attention module is built to fuse key topic information with the original text representation. The summary is then generated by combining a pointer generation network and coverage mechanism. With evaluation metrics of ROUGE-1, ROUGE-2, and ROUGE-L, the experimental results of ACGT in the English dataset CNN/Daily Mail are 0.96%, 2.44%, and 1.03% higher than the baseline model, respectively. In the Chinese dataset, LCSTS, ACGT shows a higher performance than the baseline method by 1.19%, 1.03%, and 0.85%, respectively. Our results demonstrate that the performance of summaries is significantly correlated with the number of topics that are introduced. Case studies show that the introduction of topic information can improve both the coverage of original text topics and the fluency of summaries.

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Section: Related Workmentioning

confidence: 99%

An Abstract Summarization Method Combining Global Topics

Duan

et al. 2022

Applied Sciences

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“…A text summary consists of one or more texts and covers the important information of the initial text or texts. However, the length of the summary is less than half of the length of the original text or texts, and it usually has a much smaller size [ 14 ]. Different studies vary in their approach toward summarization.…”

Section: Literature Review and Taxonomy Of Text Summarizationmentioning

confidence: 99%

Opinion texts summarization based on texts concepts with multi-objective pruning approach

2022

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Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure.

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“…Drawing inspiration from such word representations, in last years document embeddings have emerged as a natural extension of word embeddings, by mapping variable-length documents (sentences, paragraphs or full documents) to vector representations. Their effectiveness has been remarkable in a wide diversity of tasks, such as text classification and sentiment analysis (Fu et al, 2018;Le and Mikolov, 2014;Bansal and Srivastava, 2019), multi-document summarisation (Lamsiyah et al, 2021;Rani and Lobiyal, 2022), forum question duplication (Lau and Baldwin, 2016), document similarity (Dai et al, 2020), sentence pair similarity (Chen et al, 2019), and even semantic relatedness and paraphrase detection (Logeswaran and Lee, 2018).…”

Section: Introductionmentioning

confidence: 99%

Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

Blanco-Fernández,

Gil-Solla,

Pazos-Arias

et al. 2023

Informatica

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Embedding models turn words/documents into real-number vectors via co-occurrence data from unrelated texts. Crafting domain-specific embeddings from general corpora with limited domain vocabulary is challenging. Existing solutions retrain models on small domain datasets, overlooking potential of gathering rich in-domain texts. We exploit Named Entity Recognition and Doc2Vec for autonomous in-domain corpus creation. Our experiments compare models from general and in-domain corpora, highlighting that domain-specific training attains the best outcome.

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Document vector embedding based extractive text summarization system for Hindi and English text

Cited by 10 publications

References 42 publications

An Abstract Summarization Method Combining Global Topics

An Abstract Summarization Method Combining Global Topics

Opinion texts summarization based on texts concepts with multi-objective pruning approach

Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

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