A hybrid approach for automatic document summarization

Rani, S. Siji; Sreejith, K; Sanker, Arjun

doi:10.1109/icacci.2017.8125917

Cited by 15 publications

(2 citation statements)

References 3 publications

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“…The sentence scoring parameter significantly improves the performance of the model. The researchers presented a single-document text summarization technique based on sentence similarity and document context [25]. Their approach utilized undirected graph-based scoring to evaluate sentences and determine which ones should be included in the summary.…”

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%

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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“…Combining more than one feature of text summarization leads to the development of a hybrid model. Rani et al (2017) proposed a hybrid model which makes use of the word frequency and the location of the paragraphs. Words contained in paragraphs at the beginning of the document are given more weight than words in the preceding paragraphs.…”

Section: Hybrid Summarizationmentioning

confidence: 99%

A Hybrid Model for Text Summarization Using Natural Language Processing

Karanja¹,

Matheka²

2022

OJIT

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Text summarization plays an important role in the area of natural language processing. The need for information all over the world to solve specific problems keeps on increasing daily. This poses a greater challenge as data stored on the internet has gradually increased exponentially over time. Finding out the relevant data and manually summarizing it in a short time is a challenging and tedious task for a human being. Text Summarization aims to compress the source text into a more concise form while preserving its overall meaning. Two major categories of text summarization methods exist namely: extractive and abstractive. The extractive technique concentrates on determining key themes using frequency analysis of sentences in the corpus of the text. Abstractive methods write a new summary with newly generated texts which do not appear in the corpus itself. This paper presents a hybrid model for text summarization using both extractive and abstractive techniques. Term Frequency (TF) – Inverse Document Frequency (IDF) was used for extractive and T5 Transformers for abstractive summarization. Iterative Incremental Methodology was adopted in the study. The hybrid model emerged as not the best choice compared to the extractive and abstractive as it had been hypothesized in the study when the accuracy and execution time of the summary generated was considered.

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Abstractive Text Summarization on Templatized Data

Jyothi

Supriya

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A hybrid approach for automatic document summarization

Cited by 15 publications

References 3 publications

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

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

A Hybrid Model for Text Summarization Using Natural Language Processing

Abstractive Text Summarization on Templatized Data

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