Text Summarization using Partial Textual Entailment based Graphs

Kaur, Manpreet; Srivastava, Dipti

doi:10.1109/comitcon.2019.8862234

Cited by 4 publications

(14 citation statements)

References 11 publications

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“…The extractive summarization process consists of selecting key phrases, sentences, paragraphs etc. In the original document and combine those with a short form that maintains a sequential sequence [7].…”

Section: A Text Summarizationmentioning

confidence: 99%

“…So, authors have seen the quality of the summary is limited by the accuracy of the TE determination. This [7] seeks to overcome these restrictions by using Partial Textual Entailment (PTE) as well. Hence, [7] suggested a modified approach that demonstrated competitive outcomes in standard dataset.…”

Section: A Text Summarizationmentioning

confidence: 99%

“…Textual Entailment task is a problem of classification [5]. TE is the latest and best indicator of text acquisition in NLP applications [7].Recent work on Text Entailment work includes (1) models based on linked elements and machine-learning classifiers, (2) sentence-based models that use similar structures such as Semantic Textual similarity, sentence-based function, and (3) other neural network models that do not use Simple RRNs or CNNs as sentence encoders.…”

Section: Introductionmentioning

confidence: 99%

“…Relationships are active and effective when the fact of one text fragment follows from another fragment. In the TE framework, integrated and integrated texts are defined as text (T) and hypothesis (H) [7] .…”

Section: Introductionmentioning

confidence: 99%

“…Textual Entailment (TE) is a typical example of a semantic inference, in which the purpose is to determine where a textual hypothesis (H) can be correctly included in a given text (T). While classifying the hypothesis into genes and making an effort to determine where each is differentiated separately by T is called Partial Textual Entailment (PTE) [7]. Partial Textual entailment (PTE) is a possible solution to this problem that defines the interdependent relationships between T and H pairs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Elshazly¹,

Haggag²,

Ehssan³

2021

IJACSA

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Textual entailment recognition is one of the recent challenges of the Natural Language Processing (NLP) domain. Deep learning strategies are used in the work of text entailment instead of traditional Machine learning or raw coding to achieve new enhanced results. Textual entailment is also used in the substantial applications of NLP such as summarization, machine translation, sentiment analysis, and information verification. Text entailment is more precise than traditional Natural Language Processing techniques in extracting emotions from text because the sentiment of any text can be clarified by textual entailment. For this purpose, when combining a textual entailment with deep learning, they can hugely showed an improvement in performance accuracy and aid in new applications such as depression detection. This paper lists and describes applications of natural language processing regarding textual entailment. Various applications and approaches are discussed. Moreover, datasets, algorithms, resources, and performance evaluation for each model is included. Also, it compares textual entailment application models according to the method used, the result for each model, and the pros and cons of each model.

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Section: A Text Summarizationmentioning

confidence: 99%

Section: A Text Summarizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Elshazly¹,

Haggag²,

Ehssan³

2021

IJACSA

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Efficient Text Summarization using Natural Language Processing

Kumar,

Thejas

2023

2023 International Conference on Data Science, Agents &Amp; Artificial Intelligence (ICDSAAI)

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Review on Query-focused Multi-document Summarization (QMDS) with Comparative Analysis

Roy¹,

Kundu

2023

ACM Comput. Surv.

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The problem of query-focused multi-document summarization (QMDS) is to generate a summary from multiple source documents on identical/similar topics based on the query submitted by the users. The paper provided a systematic review of the literature of QMDS. The research works are classified into six major categories based on the summarization methodologies used. Different techniques used for finding query-relevant summaries for different algorithms under each of the six major groups are reported. Further, seventeen evaluation metrics used for evaluating algorithms for text summaries against the human-curated summaries are compiled here in this paper. Extensive experiments are performed on 8 different data sets. Comparative results of 9 methodologies, each representing one of the 6 different groups, are presented. Seven different evaluation metrics are used in the comparative study. It is observed that DL and ML based QMDS methods are performing better in comparison to the other methods.

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Text Summarization using Partial Textual Entailment based Graphs

Cited by 4 publications

References 11 publications

Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Efficient Text Summarization using Natural Language Processing

Review on Query-focused Multi-document Summarization (QMDS) with Comparative Analysis

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