Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Yadav, Chandra Shekhar; Sharan, Aditi

doi:10.4018/ijirr.2015100104

Cited by 19 publications

(3 citation statements)

References 25 publications

(34 reference statements)

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“…Based on this hypothesis, we are convinced that some specific features also have certain relationship with their contexts. In fact, there are already researches aiming at word sense disambiguation based on contexts . We found that training an embedding model using bigrams as basic units can map bigram feature to fixed length vectors, so that the semantic information of phrase might be captured, and for homographs and polysemy, we treat them as different words when they have different part‐of‐speech.…”

Section: The Proposed Methodsmentioning

confidence: 99%

EcForest: Extractive document summarization through enhanced sentence embedding and cascade forest

Yang

Al-Sabahi

et al. 2019

Concurrency and Computation

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We present EcForest, an extractive summarization model through Enhanced Sentence Embedding and Cascade Forest. Sentence representation is of great significance for many summarization methods. Bag-of-words mostly fails to grasp the semantics, and typical embedding models cannot capture more complex semantic features, such as polysemy and the meaning of a phrase, which is usually ignored by simply averaging the word embeddings included in a sentence. To this end, we propose Enhanced Sentence Embedding (ESE) model to solve such drawbacks via mapping several valid features to dense vectors. Essentially, the enhanced sentence embedding is a novel model for improving the distributed representation of sentence. Our sentence embedding model is universally applicable and it can be adapted to other NLP tasks. Moreover, deep forest is used as a sentence extraction algorithm for its robustness to the hyper-parameters and its efficient training algorithm compared to deep neural network.The evaluation of variant models proposed in this work proves the validation of the enhanced sentence embedding. The comparison results between EcForest and several baselines on two different datasets demonstrate that the proposed summarization model performs better than or with high competitiveness to the state-of-the-art.

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Section: The Proposed Methodsmentioning

confidence: 99%

EcForest: Extractive document summarization through enhanced sentence embedding and cascade forest

Yang

Al-Sabahi

et al. 2019

Concurrency and Computation

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“…• Sentence position: Many researchers have considered sentence position feature as scoring feature in the document because most important sentences can be seen at the beginning of the document (Babar and Patil, 2015;Ferreira et al, 2013;Yadav and Sharan, 2015). The equation which we are using to calculate the sentence location score is shown in equation 6.…”

Section: Sentence Featurementioning

confidence: 99%

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Patel

Chhinkaniwala

2018

IJKEDM

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Text summarisation is compressed or condensed version of any text document. Due to increasing use of digitisation, massive amount of information is available on internet. Text summarisation is an emerging alternative for users to find relative information in automated shortened versions. In this paper we propose single document summarisation technique using shallow features of sentence to generate summary. The weight of sentences is calculated by applying score of different words and sentence-based statistical features. Here, most salient sentences are selected based on weight of sentences and are put together to generate summary. This is modeled using fuzzy inference system. This approach utilises fuzzy inference and fuzzy measures to find most significant sentences. The result of our proposed method is compared with other methods using recall oriented understudy for Gisting evaluation (ROUGE-N) measures on document understanding conferences (DUC) 2002 dataset and results show that our proposed method outperforms a few baseline methods.

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“…Scientific papers and government reports are examples of lengthy texts that frequently explore important topics in -depth and take lot of time to read [11]. Text document summarizing is referred to as summarization process and is crucial component of inform ation retrieval since it reduces big body of information into manageable amounts by choosing most significant sentences and omitting unnecessary ones [12]. Problem of information overload is solved by text summarizing that locates and selects important sentences from documents to create text summaries automatically.…”

Section: Introductionmentioning

confidence: 99%

HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

Aluri,

2023

Preprint

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Accessing information from online has become difficult problem due to the internet's explosive rise in textual resources. User’s frequently sought for topic summaries from multiple sources to satisfy their informational demands. Single document abstractive summarization is significant role in Natural Language Processing (NLP) aiming to generate concise summary of source text from single document. Method of producing natural language summaries from text, keeping key points of the input document as such is a challenging task. In this research, hybrid optimization algorithm, namely proposed Hunter Sail Fish Optimizer (HSFO) is used leading to abstractive summarization. Here, acquired document is allowed for Semantic Role Labeling (SRL), at which Stanza tool is used for extraction of Predicate Argument Structures (PAS). Next to SRL, optimized features are generated by computation of semantic similarity using Wave-Hedges metrics. Moreover, semantic feature clustering of PAS is performed using Bayesian Fuzzy Clustering (BFC). Then, feature score is generated by utilization of HSFO for parameter selection, leading to abstractive summarization by Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). Here, HSFO is devised by combining Hunter-Prey Optimizer (HPO) and Sail Fish Optimizer (SFO). The dataset used in this work is Telugu dataset, from which text document in the sentence form is acquired. Finally, performance of HSFO_LSTM-CNN is analyzed by using four performance measures, precision, recall, F-measure, and Rouge, which shows superior values of precision as 0.887, recall as 0.925, F-measure as 0.906, and rouge as 0.815.

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Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Cited by 19 publications

References 25 publications

EcForest: Extractive document summarization through enhanced sentence embedding and cascade forest

EcForest: Extractive document summarization through enhanced sentence embedding and cascade forest

Fuzzy logic-based single document summarisation with improved sentence scoring technique

HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

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