Improving Text Summarization using Ensembled Approach based on Fuzzy with LSTM

Tomer, Minakshi; Kumar, Manoj

doi:10.1007/s13369-020-04827-6

Cited by 31 publications

(6 citation statements)

References 28 publications

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“…The application of fuzzy logic in text summarization technology mostly uses fuzzy logic to improve the method of extracting features and the relevant dimensions and attributes of features, Padmapriya & Duraiswamy use deep learning combined with fuzzy logic to improve the efficiency of text summarization [78], Malallah & Ali proposed a new English multi-text summary model, which will eventually extract language and related statistical features from documents for training fuzzy logic systems, and then use the Apriori algorithm to extract important features for association rules and use them for subsequent text summaries [79], Du & Huo proposed an automatic summarization model for news texts using fuzzy association rules to operate on multi-text features and using genetic algorithm (GA) to adjust the weighted weight [80], Tomer & Kumar proposed a novel hybrid summarization method that uses fuzzy logic rules to extract sentence features in documents and uses bidirectional longterm short-term memory (BiLSTM) in deep learning for summarization [81], Patel et al proposed a multi-document summarization model based on multi-information features, the features are extracted based on statistical methods and fuzzy logic is used to deal with the inaccuracy and uncertainty of feature weights, and then removing redundancy in abstract output uses cosine similarity to measure the similarity in abstract words [82].…”

Section: Text Summarizationmentioning

confidence: 99%

Review of Survey Research in Fuzzy Approach for Text Mining

Lai

Chen

2023

IEEE Access

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Text mining has been common research field, with the emergence of Web 2.0 and the development of social software, the amount of text generated every day has increased dramatically. The texts contain a lot of valuable information, how to analysis for the information from text is very important. Therefore, many research have explored related methods and various fields of text mining, such as sentiment analysis, text clustering, text summarization, etc., However, unlike other numerical data that can be directly calculated in terms of character performance, the calculation must be performed after vector conversion, and the words may have polysemy, which provides more challenges for natural language processing. Due to the above challenges, various techniques are used for data preprocessing before analysis. In addition to the common statistical and discrete methods for text data, methods based on fuzzy logic provide another option for effective natural language analysis, Therefore, in recent years, more and more studies have added Fuzzy logic to additionally capture the context semantics of individual words to help more accurate natural language processing. This survey research discusses multiple text mining methods, The survey paper discusses multiple text mining methods, subfields and application fields, covering the literature published between 2010 and 2022, It is organized to the subtasks to be performed, the methods and natural language processing techniques used, and the application scenarios. At the end of this study, the research has provided the key point discussion and relevant suggestions for text mining combined with fuzzy logic.

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Section: Text Summarizationmentioning

confidence: 99%

Review of Survey Research in Fuzzy Approach for Text Mining

Lai

Chen

2023

IEEE Access

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“…The human evaluation shows that the model also meets the user's expectations.. Fuzzy logic rules have been used by Tomer et al [26] in order to generate summaries of some given text. The researcher adds the fuzzy logic rules to the bidirectional LSTM to create the model FLSTM.…”

Section: Sequence-to-sequence Model Basedmentioning

confidence: 99%

A Brief Survey on Natural Language Processing Based Text Generation and Evaluation Techniques

Philip,

Minhas

2022

VFAST trans. softw. eng.

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Text Generation is a pressing topic of Natural Language Processing that involves the prediction of upcoming text. Applications like auto-complete, chatbots, auto-correct, and many others use text generation to meet certain communicative requirements. However more accurate text generation methods are needed to encapsulate all possibilities of natural language communication. In this survey, we present cutting-edge methods being adopted for text generation. These methods are divided into three broad categories i.e. 1) Sequence-to-Sequence models (Seq2Seq), 2) Generative Adversarial Networks (GAN), and 3) Miscellaneous. Sequence-to-Sequence involves supervised methods, while GANs are unsupervised, aimed at reducing the dependence of models on training data. After this, we also list a few other text generation methods. We also summarize some evaluation metrics available for text generation and their Performance

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“…The LSTM shows promise in producing a concise abstractive summary. [5], [289], [290] used the LSTM-based method to summarize risk. 3) Gated Recurrent Unit (GRU): GRU is a simplified LSTM with two gates: a reset gate and an update gate with no explicit memory.…”

Section: ) Deep Learning Algorithmmentioning

confidence: 99%