SHEG: summarization and headline generation of news articles using deep learning

Singh, Rajeev Kumar; Khetarpaul, Sonia; Gorantla, Rohan; Allada, Sai Giridhar

doi:10.1007/s00521-020-05188-9

Cited by 17 publications

(2 citation statements)

References 28 publications

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“…LEAD-X which is a simple baseline uses the top X words as a result where the value of X is 10 for the headline task and 30 for the summary generation in this experiment. SHEG is proposed to produce both an abstractive summary and a headline in a supervised manner by selecting salient phrases and combing a pointer-generator network with a controlled actor-critic model [27]. Pointer-Generator with Coverage (PGC) is another supervised model based on a hybrid sequence-to-sequence attentional model [24].…”

Section: Performance Resultsmentioning

confidence: 99%

Generative Adversarial Network for Joint Headline and Summary Generation

et al. 2022

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With the ever-increasing amount of electronic documents being generated, it is imperative to provide an intuitive headline and a concise summary of the document to help readers quickly get the gist without going through the all details. While humans have strong abilities to create headlines and generate summaries, the automatic text generation of this research field is still challenging due to the difficult language understanding and complex text synthesis. Moreover, human annotation for machine learning is another matter needed to be addressed. In this paper, we propose a joint model to resolve aforementioned issues simultaneously. To perform unsupervised training to reduce labor cost, we employ a generative adversarial network (GAN) without parallel training data. Considering that the headline and summary have strong relationship, we propose a joint model to learn better representations by including an additional type representation into the GAN. Experiments are conducted on the public dataset, NEWS-ROOM. The experimental results demonstrate that our approach is able to effectively create a reasonable headline as well as a concise summary.

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Section: Performance Resultsmentioning

confidence: 99%

Generative Adversarial Network for Joint Headline and Summary Generation

et al. 2022

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“…Additionally, the overmuch Long-data documents in the most cases leading to model Deterioration [3]. Summarizing process of the News articles is an essential issue for industry the Newspapers where it is experiencing serious challenges and this turns to the intense competition from electronic media [4]. According to a report had published in 2018, the yearly market of newspaper in (USA)had evaluated at twenty seven billion $, and it is evaluated that this value would fall to seventeen billion $ by coming 2025.…”

Section: Introductionmentioning

confidence: 99%

Text summarization of News Articles based on named entity recognition using Spacy library

Alshibly

Al-Shorfat

Otair

et al. 2023

Preprint

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The study discusses the importance of summarization in dealing with a large amount of data available on the internet. The study used a deep-learning algorithm based on functions from the spacy library in Python to summarize news articles and evaluated the impact of named entity recognition on the summarization process. The study assessed different datasets from CNN-DailyMail and the BBC (entertainment articles) and found that the proposed method based on named entity recognition showed significant improvement in recall, precision, and F-score compared to the word frequency method. The study also observed that the articles from CNN-DailyMail were longer, with an average of 551 words and 28 sentences, compared to the BBC (entertainment articles), which had an average of 190 words and 12 sentences. The evaluation results showed that the proposed method based on named entity recognition performed better on the shorter articles from the BBC, indicating that the method was more effective in summarizing shorter texts. In summary, the study highlighted the importance of summarization in dealing with a large amount of data available on the internet. It showed that named entity recognition can significantly improve the effectiveness of the summarization process. The study also observed that the proposed method was more effective in summarizing shorter texts.

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