EdgeSumm: Graph-based framework for automatic text summarization

El-Kassas, Wafaa S.; Salama, Cherif; Rafea, Ahmed; Mohamed, Hoda K.

doi:10.1016/j.ipm.2020.102264

Cited by 50 publications

(12 citation statements)

References 19 publications

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“…The traditional summarization method is an extractive graph-based ranking algorithm proposed as LEXRANK [11,12]. These algorithms limit syntactic and semantic structure.…”

Section: Related Workmentioning

confidence: 99%

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Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

Sharmila¹,

Deisy²,

Parthasarathy³

2023

Computer Systems Science and Engineering

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With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty is to parallelize the sequence computation training by incorporating feed-forward, the self-normalized neural network in the Extractive phase using Intra Cosine Attention Similarity (Ext-ICAS) with sentence dependency position. Also, it does not require any normalization technique explicitly. Our proposed abstractive Bidirectional Long Short Term Memory (Bi-LSTM) encoder sequence model performs better than the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder with minimum training loss and with fast convergence. The proposed model was evaluated on the Cable News Network (CNN)/Daily Mail dataset and an average rouge score of 0.435 was achieved also computational training in the extractive phase was reduced by 59% with an average number of similarity computations.

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“…The traditional summarization method is an extractive graph-based ranking algorithm proposed as LEXRANK [11,12]. These algorithms limit syntactic and semantic structure.…”

Section: Related Workmentioning

confidence: 99%

“…From the alignment vector obtained in the previous step, these words, which meet certain threshold values are selected in the form of a context vector using Eq. (12).…”

Section: Attentionmentioning

confidence: 99%

Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

Sharmila¹,

Deisy²,

Parthasarathy³

2023

Computer Systems Science and Engineering

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“…By exploiting semantic information of sentences, Han et al [24] proposed a semantic graph model. El-Kassas et al [25] proposed a graph-based framework by combining many extractive methods.…”

Section: Literature Reviewmentioning

confidence: 99%

TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Rakrouki¹,

Alharbe²,

Khayyat³

et al. 2023

Computer Systems Science and Engineering

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Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

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“…In 2019, Joshi, Eduardo, Enrique, and Laura proposed SummCoder [11], an unsupervised framework for extractive text summarization based on deep auto-encoders.…”

Section: Summarization Approachesmentioning

confidence: 99%

Performance Study on Extractive Text Summarization Using BERT Models

Abdel-Salam

Rafea

2022

Information

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The task of summarization can be categorized into two methods, extractive and abstractive. Extractive summarization selects the salient sentences from the original document to form a summary while abstractive summarization interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches in the literature, including statistical-, graph-, and deep learning-based approaches. Deep learning has achieved promising performances in comparison to the classical approaches, and with the advancement of different neural architectures such as the attention network (commonly known as the transformer), there are potential areas of improvement for the summarization task. The introduction of transformer architecture and its encoder model “BERT” produced an improved performance in downstream tasks in NLP. BERT is a bidirectional encoder representation from a transformer modeled as a stack of encoders. There are different sizes for BERT, such as BERT-base with 12 encoders and BERT-larger with 24 encoders, but we focus on the BERT-base for the purpose of this study. The objective of this paper is to produce a study on the performance of variants of BERT-based models on text summarization through a series of experiments, and propose “SqueezeBERTSum”, a trained summarization model fine-tuned with the SqueezeBERT encoder variant, which achieved competitive ROUGE scores retaining the BERTSum baseline model performance by 98%, with 49% fewer trainable parameters.

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EdgeSumm: Graph-based framework for automatic text summarization

Cited by 50 publications

References 19 publications

Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Performance Study on Extractive Text Summarization Using BERT Models

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