Single document summarization using the information from documents with the same topic

Mao, Xiangke; Huang, Shaobin; Shen, Li; Li, Rongsheng; Yang, Hui

doi:10.1016/j.knosys.2021.107265

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…Text summarization is the task of compressing a text by automated means while preserving meaning. Various NLP approaches exist for text summarization (Mao et al, 2021 ; Suleiman and Awajan, 2022 ). There are two ways of achieving text summarization: extractive and abstractive .…”

Section: Related Workmentioning

confidence: 99%

SATS: simplification aware text summarization of scientific documents

Zaman,

Kamiran,

Shardlow

et al. 2024

Front. Artif. Intell.

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Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarization aims to shorten long documents, simplification seeks to reduce the complexity of a document. To accomplish these tasks collectively, there is a need to develop machine learning methods to shorten and simplify longer texts. This study presents a new Simplification Aware Text Summarization model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarization model, by enhancing the objective function using a word frequency lexicon for simplification tasks. We have evaluated the performance of SATS on a recently published text summarization and simplification corpus consisting of 5,400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarization, and joint simplification-summarization across two datasets on ROUGE, SARI, and CSS1. We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from eight annotators for grammar, coherence, consistency, fluency, and simplicity. The average human judgment for all evaluated dimensions lies between 4.0 and 4.5 on a scale from 1 to 5 where 1 means low and 5 means high.

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Section: Related Workmentioning

confidence: 99%

SATS: simplification aware text summarization of scientific documents

Zaman,

Kamiran,

Shardlow

et al. 2024

Front. Artif. Intell.

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show abstract

“…As a result, several methodologies have integrated multi-granular information with graph models to amplify text summarization effectiveness. In the present study, a sentence-topic-word structure has been established, proving instrumental in addressing cross-sentence dependencies for both single-document and multi-document summarization tasks ( Chen, 2023 ; Mao et al, 2021 ; Shafiq et al, 2023 ). Moreover, an approach employing adaptive breadth and depth has been implemented to update nodes of varied granularity, thereby facilitating a more effective aggregation of node-specific information.…”

Section: Related Workmentioning

confidence: 99%

Multi-granularity adaptive extractive document summarization with heterogeneous graph neural networks

Su,

Jiang,

Huang

2023

PeerJ Computer Science

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The crucial aspect of extractive document summarization lies in understanding the interrelations between sentences. Documents inherently comprise a multitude of sentences, and sentence-level models frequently fail to consider the relationships between distantly-placed sentences, resulting in the omission of significant information in the summary. Moreover, information within documents tends to be distributed sparsely, challenging the efficacy of sentence-level models. In the realm of heterogeneous graph neural networks, it has been observed that semantic nodes with varying levels of granularity encapsulate distinct semantic connections. Initially, the incorporation of edge features into the computation of dynamic graph attention networks is performed to account for node relationships. Subsequently, given the multiplicity of topics in a document or a set of documents, a topic model is employed to extract topic-specific features and the probability distribution linking these topics with sentence nodes. Last but not least, the model defines nodes with different levels of granularity—ranging from documents and topics to sentences—and these various nodes necessitate different propagation widths and depths for capturing intricate relationships in the information being disseminated. Adaptive measures are taken to learn the importance and correlation between nodes of different granularities in terms of both width and depth. Experimental evidence from two benchmark datasets highlights the superior performance of the proposed model, as assessed by ROUGE metrics, in comparison to existing approaches, even in the absence of pre-trained language models. Additionally, an ablation study confirms the positive impact of each individual module on the model's ROUGE scores.

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Improved hybrid text summarization system using deep contextualized embeddings and statistical features

Gambhir,

Gupta

2024

Multimed Tools Appl

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Single document summarization using the information from documents with the same topic

Cited by 9 publications

References 25 publications

SATS: simplification aware text summarization of scientific documents

SATS: simplification aware text summarization of scientific documents

Multi-granularity adaptive extractive document summarization with heterogeneous graph neural networks

Improved hybrid text summarization system using deep contextualized embeddings and statistical features

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