Text Summarization Using Document and Sentence Clustering

Pawar, Sumathi; Gururaj, H Manjula; Chiplunar, Niranajan N

doi:10.1016/j.procs.2022.12.038

Cited by 7 publications

References 8 publications

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Tokenizing Innovation: A Blockchain‐Based Innovation System as a Step Toward Achieving Sustainable Development

Xie,

Alnafrah,

Dagestani

2025

Sustainable Development

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This study examines patenting patterns to identify inefficiencies in innovation systems and proposes a blockchain‐based solution to advance sustainable development goals (SDGs) 4, 8, 9, and 17. Using a dataset of 13,910 patent documents, the research employs a multi‐step methodology: a patent landscape analysis, network analysis, and structural equation modeling (SEM). Our findings indicate that patents linked to non‐competitive technologies and low originality scores are more likely to become inactive. Furthermore, the study identifies several factors contributing to the low efficiency of the innovation system, including closed technology transfer processes and weak collaborative synergies among stakeholders. To address these challenges, a blockchain‐based rewards system is proposed to foster beneficial relationships among innovation stakeholders, supporting sustainable innovation practices. This study enhances our understanding of innovation theory and provides insights for policymakers, linking the proposed solution to achieving SDGs 4, 8, 9, and 17 through sustainable innovation.

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Tokenizing Innovation: A Blockchain‐Based Innovation System as a Step Toward Achieving Sustainable Development

Xie,

Alnafrah,

Dagestani

2025

Sustainable Development

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Graphs in clusters: a hybrid approach to unsupervised extractive long document summarization using language models

Gokhan,

Price,

Lee

2024

Artif Intell Rev

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Effective summarization of long documents is a challenging task. When addressing this challenge, Graph and Cluster-Based methods stand out as effective unsupervised solutions. Graph-Based Unsupervised methods are widely employed for summarization due to their success in identifying relationships within documents. Cluster-Based methods excel in minimizing redundancy by grouping similar content together before generating a concise summary. Therefore, this paper merges Cluster-Based and Graph-Based methods by applying language models for Unsupervised Extractive Summarization of long documents. The approach simultaneously extracts key information while minimizing redundancy. First, we use BERT-based sentence embeddings to create sentence clusters using k-means clustering and select the optimum number of clusters using the elbow method to ensure that sentences are categorized based on their semantic similarities. Then, the TextRank algorithm is employed within each cluster to rank sentences based on their importance and representativeness. Finally, the total similarity score of the graph is used to rank the clusters and eliminate less important sentence groups. Our method achieves comparable or better summary quality and reduced redundancy compared to both individual Cluster-Based and Graph-Based methods, as well as other supervised and Unsupervised baseline models across diverse datasets.

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Text summarization using residual-based temporal attention convolutional neural network

Rajan,

Jose

2023

Int. j. inf. tecnol.

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Text Summarization Using Document and Sentence Clustering

Cited by 7 publications

References 8 publications

Tokenizing Innovation: A Blockchain‐Based Innovation System as a Step Toward Achieving Sustainable Development

Tokenizing Innovation: A Blockchain‐Based Innovation System as a Step Toward Achieving Sustainable Development

Graphs in clusters: a hybrid approach to unsupervised extractive long document summarization using language models

Text summarization using residual-based temporal attention convolutional neural network

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