Study of automatic text summarization approaches in different languages

Kumar, Yogesh; Kaur, Komalpreet; Kaur, Sukhpreet

doi:10.1007/s10462-021-09964-4

Cited by 33 publications

(7 citation statements)

References 60 publications

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“…The formulae to compute this network is shown in Equations (( 10 ), ( 11 ), and ( 12 )). where is the backward state GRU, is the forward state GRU, ⊕ indicates the concatenation operation of two vectors, is input at time t 29 , 30 .…”

Section: Methodsmentioning

confidence: 99%

Automated detection and recognition system for chewable food items using advanced deep learning models

Kumar,

Koul,

Kamini

et al. 2024

Sci Rep

Self Cite

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Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

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Section: Methodsmentioning

confidence: 99%

Automated detection and recognition system for chewable food items using advanced deep learning models

Kumar,

Koul,

Kamini

et al. 2024

Sci Rep

Self Cite

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show abstract

“…Anand and Wagh (Anand and Wagh, 2022) explored effective deep learning approaches tailored for summarizing legal texts, highlighting the importance of domain-specific summarization solutions. Multilingual summarization aims to handle diverse languages beyond English, with Kumar et al (Kumar et al, 2021) surveying techniques for languages such as Chinese, Arabic, Persian, Hindi, Tamil, and Bengali. Neural network approaches have shown promise in this domain by relying less on language-specific resources.…”

Section: Multilingual and Query-based Summarizationmentioning

confidence: 99%

Automatic Text Summarization Using Deep Learning Methods

Shopnil,

Das,

Saha

2024

Preprint

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Text document summarization is critical for managing today’s vast textual data. This paper presents an approach to text document summarization that does not rely on word embedding techniques. Instead, our method follows a step-by-step process, including sentence segmentation, sentence embedding, K-means clustering, and summary generation. The input text is segmented into individual sentences using an NLP tool such as NLTK’s sentence tokenizer. Next, we extract contextual embeddings for each sentence using the Sentence Transformer method. These embeddings capture the meaning of each sentence within the context of the surrounding text. The sentence embeddings are then subjected to K-means clustering. This step enables the creation of clusters that represent semantically related sentences. To generate the summary, depending on how far each sentence is from the cluster centroid, we choose one sentence from each cluster. The sentence with the lowest distance from the centroid is chosen, and the selected sentences are ordered as they appeared in the original text. We implemented the summarizer and evaluated its performance on the DUC 2007 dataset, a collection of news articles with manually crafted summaries by human experts. The results demonstrate that our summarizer produces informative and concise summaries, surpassing a baseline approach that solely extracts top-ranked sentences from the input text. Our work contributes to text document summarization by presenting an alternative approach that does not rely on word embedding techniques. By leveraging sentence segmentation, contextual embeddings, K-means clustering, and centroid-based selection, our method offers a viable solution for generating high-quality summaries. Further research can explore enhancements to our approach and its application in various domains where text summarization is essential.

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“…This was because the dataset was based on human summaries. Kumar et al [15] introduced a model for building a network in which text phrases are depicted as nodes and the relationship between different sentences were represented as the weight of the edge linking them. In contrast to traditional cosine similarity, which treats words identically, a modified reversed sentence frequency-cosine similarity was constructed to assign various weights to distinct terms in the document.…”

Section: Extractive Text Summarizationmentioning

confidence: 99%

EASESUM: an online abstractive and extractive text summarizer using deep learning technique

Adeniyi,

Ajagbe,

Adeniyi

et al. 2024

IJ-AI

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<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p class="CM12">Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.</p></td></tr></tbody></table></div>

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Study of automatic text summarization approaches in different languages

Cited by 33 publications

References 60 publications

Automated detection and recognition system for chewable food items using advanced deep learning models

Automated detection and recognition system for chewable food items using advanced deep learning models

Automatic Text Summarization Using Deep Learning Methods

EASESUM: an online abstractive and extractive text summarizer using deep learning technique

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