Automatic text summarization of news articles

Sethi, Prakhar; Sonawane, Sameer; Khanwalker, Saumitra; Keskar, Ravindra B.

doi:10.1109/bid.2017.8336568

Cited by 38 publications

(7 citation statements)

References 7 publications

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“…Tere is an abundance of text-based content on the Internet, including web publications, papers, news, and reviews, that must be summarised to get the document's gist [18]. ATS has many uses, such as short read generation, passage reduction, compaction, extracting, and the most important information from sensitive reports, including legal reports produced by legal authorities [19]. ATS can also be used in news text summarizers to assist readers in fnding the most interesting and important content in less time [20][21][22].…”

Section: Text Summarizationmentioning

confidence: 99%

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

Umair

Alam

Khan

et al. 2022

Computational Intelligence and Neuroscience

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The extractive summarization approach involves selecting the source document’s salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model.

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Section: Text Summarizationmentioning

confidence: 99%

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

Umair

Alam

Khan

et al. 2022

Computational Intelligence and Neuroscience

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“…Part of speech tagging is done in order to identify nouns, as nouns are used to build lexical chain using Silber and McCoy's method. Sethi, P et al [5] also proposes new scoring criterions that is used to identify important parts which will eventually make it to the summary. Based on the aforementioned scoring criterion, the important lexical chains are identified.…”

Section: IImentioning

confidence: 99%

Temporal Condensation of Tamil News

Shreenidhi

Ranganathan

2021

etj

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Since the dawn of the Internet, we have been inundated with an excess of information. The volume of information available on the Internet is expected to grow exponentially. This brings a need for summarization of information. Thus, making summarization one of the most sought-after topics in the domain of natural language processing. It is essential to be informed about the vital happenings, and newspapers have been serving this purpose for a very long time. Sadly, there is a perception among the general public that no news agency today can be unequivocally trusted, the credibility of news articles is uncertain. Therefore, one has to read news articles from various sources to get an unbiased view on topic. When a query related to an event is entered in SEs like google, the search renders an overwhelming number of responses, it is humanly impossible to read all of them. In an effort to address the aforementioned problems, a condensation of news articles covering the Tamilnadu Legislative Assembly election is performed. The news articles were collected from various news sources over a period of two months. The collected articles were translated from Tamil to English. These articles included news about various events, in order to segregate Tamilnadu related news from them k-means clustering was performed on the dataset. The relvant news articles acquired was pre-processed to remove ambiguity and mistakes from translation. These articles were summarized individually using a linear regression model that gave importance to features such as named entities, number of words that were similar to title etc. The acquired individual summaries were summarized using BERT extractive summarizer as it would reduce redundancy. When generated summary was compared with introduction and title of the article in the absence of an introduction a precision of 0.512, recall of 0.25 and f-measure of 0.31 were obtained.

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“…The Pre Processor module performs various NLP tasks that include tokenization, dependency parsing, sentence splitting and named entity recognition. For these NLP tasks, we use the open source library Spacy 4 .…”

Section: Preprocessing Graph Summary and Knowledge Base Constructionmentioning

confidence: 99%

“…This problem is widely studied in the literature and expands to multiple categories under the news systems implementation. This implementation is in the intersection of news collection, first story detection [1], events detection [2] and sub-event detection [3], news articles summarization [4], opinion mining [5], trends detection [6] and fake news detection [7].…”

Section: Introductionmentioning

confidence: 99%

News Monitor: A Framework for Exploring News in Real-Time

Panagiotou

Saravanou

Gunopulos

2021

Data

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News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a wide variety of online news portals and performs various analysis tasks. The framework initially identifies fresh news (first stories) and clusters articles about the same incidents. For every story, at first, it extracts all of the corresponding triples and, then, it creates a knowledge base (KB) using open information extraction techniques. This knowledge base is then used to create a summary for the user. News Monitor allows for the users to use it as a search engine, ask their questions in their natural language and receive answers that have been created by the state-of-the-art framework BERT. In addition, News Monitor crawls the Twitter stream using a dynamic set of “trending” keywords in order to retrieve all messages relevant to the news. The framework is distributed, online and performs analysis in real-time. According to the evaluation results, the fake news detection techniques utilized by News Monitor allow for a F-measure of 82% in the rumor identification task and an accuracy of 92% in the stance detection tasks. The major contribution of this work can be summarized as a novel real-time and scalable architecture that combines various effective techniques under a news analysis framework.

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Automatic text summarization of news articles

Cited by 38 publications

References 7 publications

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

Temporal Condensation of Tamil News

News Monitor: A Framework for Exploring News in Real-Time

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