Lattice abstraction-based content summarization using baseline abstractive lexical chaining progress

Mohan, G. Bharathi; Kumar, R. Prasanna

doi:10.1007/s41870-022-01080-y

Cited by 22 publications

(1 citation statement)

References 31 publications

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“…There are many attention-based mechanisms to generate an article summary; one such method is attentive encoder-based summarization (AES) combined with unidirectional recurrent neural network (Uni-AES) [33]. Mohan et al [34], [35] also compared the performance of bidirectional recurrent neural network (Bi-AES) with Uni-AES and experiment results showed that Bi-AES shows better results than Uni-AES.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

Gurusamy,

Rengarajan,

Srinivasan

2023

IJECE

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<span lang="EN-US">Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.</span>

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Section: Literature Reviewmentioning

confidence: 99%