SumItUp: A Hybrid Single-Document Text Summarizer

Bhat, Iram Khurshid; Mohd, Mudasir; Hashmy, Rana

doi:10.1007/978-981-10-5687-1_56

Cited by 20 publications

(7 citation statements)

References 7 publications

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“…Experiments were performed on two separate email corpora that were publicly available, and the system was evaluated using Rouge. Lately, [18] has used attentional encoder-decoder recurrent neural networks to frame their abstractive automatic text summarizer. This work also attempted to address serious problems occurring in the basic model by proposing a few novel models.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Abstractive and Extractive Summarization Methods

Kirmani,

Kaur,

Mohd

2024

Int. J. Emerg. Technol. Learn.

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This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, the output summary is a subset of the input text and is generated by using the sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. A comparative study of these methods is also highlighted.

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Section: Related Workmentioning

confidence: 99%

Analysis of Abstractive and Extractive Summarization Methods

Kirmani,

Kaur,

Mohd

2024

Int. J. Emerg. Technol. Learn.

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“…The unsupervised technique employed in the literature includes the works like [10][11][12]. They use different features to rank sentences and produce extractive summaries.…”

Section: Unsupervised Techniquesmentioning

confidence: 99%

Biomedical semantic text summarizer

Kirmani,

Kour,

Mohd

et al. 2024

BMC Bioinformatics

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Background Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. Results This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. Conclusion The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

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“…Then it recreates the sentences with new words. Summary content: indicative An indicative summary does not include the substance of the input text, but rather indicates the basic subject matter or domain of the text [7]. It simply gives an idea of the source document whether to read the text or not.…”

Section: Hybrid Approachmentioning

confidence: 99%

A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization

Sharma,

Aggarwal,

Rai

2024

TELKOMNIKA

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Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? Therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain.

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SumItUp: A Hybrid Single-Document Text Summarizer

Cited by 20 publications

References 7 publications

Analysis of Abstractive and Extractive Summarization Methods

Analysis of Abstractive and Extractive Summarization Methods

Biomedical semantic text summarizer

A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization

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