Graph Based Extractive News Articles Summarization Approach leveraging Static Word Embeddings

Barman, Utpal; Barman, Vishal; Rahman, M.; Choudhury, Nawaz Khan

doi:10.1109/compe53109.2021.9752056

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…ROUGE-1, ROUGE-2, ROUGE-L, BLEU-1, BLEU-2, BLEU-3, BLEU-4, F-measure (of 1, 2 and 3 grams), WEEM4TSw, WEEM4TSg and WEEM4TSf. Barman et al (2021) It represents each word by GLOVE vectors and each sentence by the average of the words it contains. In order to build the network, each sentence represents a node and the weights of the edges are evaluated by the cosine similarity.…”

Section: Newsroom Summarization Datasetmentioning

confidence: 99%

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

Cajueiro¹,

Nery²,

Tavares³

et al. 2023

Preprint

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We provide a literature review about Automatic Text Summarization (ATS) systems. We consider a citation-based approach. We start with some popular and well-known papers that we have in hand about each topic we want to cover and we have tracked the "backward citations" (papers that are cited by the set of papers we knew beforehand) and the "forward citations" (newer papers that cite the set of papers we knew beforehand). In order to organize the different methods, we present the diverse approaches to ATS guided by the mechanisms they use to generate a summary. Besides presenting the methods, we also present an extensive review of the datasets available for summarization tasks and the methods used to evaluate the quality of the summaries. Finally, we present an empirical exploration of these methods using the CNN Corpus dataset that provides golden summaries for extractive and abstractive methods.

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Section: Newsroom Summarization Datasetmentioning

confidence: 99%

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

Cajueiro¹,

Nery²,

Tavares³

et al. 2023

Preprint

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“…Many attention has been given to the use of word embedding in TextRank algorithm for text summarization [12]- [16]. These works use different kinds of word embeddings, such as Global Vectors for word representation (GloVe) [12], [14], [17], Word2Vec [13], [14], FastText [14], and sentence-BERT (SBERT) [15], [16]. All of these works, however, did not use weighted word embedding, so the embedding is not weighed according to the collection statistics.…”

Section: Introductionmentioning

confidence: 99%

Enhanced TextRank using weighted word embedding for text summarization

Yulianti,

Pangestu,

Jiwanggi

2023

IJECE

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<p><span lang="EN-US">The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.</span></p>

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Deep Neural Network-Based Smart Grid Stability Analysis: Enhancing Grid Resilience and Performance

Lahon,

Kandali,

Barman

et al. 2024

Energies

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With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California’s machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems.

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Graph Based Extractive News Articles Summarization Approach leveraging Static Word Embeddings

Cited by 3 publications

References 8 publications

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

Enhanced TextRank using weighted word embedding for text summarization

Deep Neural Network-Based Smart Grid Stability Analysis: Enhancing Grid Resilience and Performance

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