Text Summarization Approach Research Articles

An Exploration of Extractive method and Abstractive Method of Text Summarization with Various Approaches, Techniques and Datasets

Abstract: In today’s era, an enormous amount of data available which is in complex form such as Social media content, Images, Audio, Video and Text data form. The mechanism is very much needed to provide these types of data in simple and easy to understandable form. In this paper, importance is given on text summarization of large amount of textual data of documents must be specified in concise and understandable form. Automatic Text Summarization is the technique which is used to represent most significant concepts in precise and comprehensible form from original or source document to target document providing greater flexibility and convenience. Since the advancement in emerging technologies leads to the challenges in research field which is leading towards to need arises to analyze and should be resolved over the time period. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. Automatic text summarization process model can be divided into three steps in the preprocessing step, source text interpretation to representation, source text representation transform to summary text representation with an algorithm. Finally, summary text generation from summary representation. In this paper discussion were made about some of the extractive based and abstractive based methods of text summarization for single documents. Various approaches of text summarization, methods needed for it along with tools and techniques, metrics needed to measure quality of generated text summary applications have been discussed in detail. This survey paper highlights the main advancement in the field of Automatic Text Summarization and provides the key methods with their benefits and limitations. Gives an overview of the past, present and future direction with certain solution to the challenges arises in text summary generation process with extractive and abstractive methods.

Over comparative study of text summarization techniques based on graph neural networks

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.

“An Extensive study of Symantic and Syntatic Approaches to Automatic Text Summarization”

Automatic text summarization (ATS) has emerged as a crucial research domain in the discipline of natural language processing (NLP) and information retrieval. The exponential growth of digital content has necessitated the need for efficient techniques that can automatically generate concise and informative summaries from lengthy documents. This article provided a comprehensive recap of automatic text summarization, covering both abstractive and extractive methods. Using extractive techniques, prime phrases or keywords from the original text are identified and chosen, while abstractive methods involve producing summaries by paraphrasing and synthesizing content in a more human-like manner. Discussed the advantages and limitations of each approach, including the challenge of ATS, which arises when summarizing content from external sources. Furthermore, reviews common evaluation metrics used for assessing the quality of summaries and discusses recent advancements in neural network-based approaches for text summarization. This survey aims to provide an overview of automatic text summarization which acts as a useful resource for researchers and practitioners in the fields of information retrieval and NLP.

Automatic text summarization of scientific articles using transformers—A brief review

<p>Learning how to read research papers is a skill. The researcher must go through many published articles during the research. It is a challenging and tedious task to go through numerous published articles. The research process would sped up by automatic summarization of scientific publications, which would aid researchers in their investigation. However automatic text summarization of scientific research articles is difficult due to its distinct structure. Various text summarization approaches have been proposed for research article summarization in the past. After the invention of transformer architecture, it has created a big shift in Natural Language Processing. The models based on transformers are able to achieve state-of-the-art results in text summarization. This paper provides a brief review of transformer-based approaches used for text summarization of scientific research articles along with the available corpus and evaluation methods that can be used to assess the model generated summary. The paper also discusses the future direction and limitations in this field.</p>

A Hybrid Approach for Automatic Text Summarization by Handling Out-of-Vocabulary Words Using TextR-BLG Pointer Algorithm

A Hybrid Approach for Automatic Text Summarization by Handling Out-of-Vocabulary Words Using TextR-BLG Pointer Algorithm

A Multi-Metric Model for analyzing and comparing extractive text summarization approaches and algorithms on scientific papers

In today's world, where data and information are increasingly proliferating, text summarization and technologies play a critical role in making large amounts of text data more accessible and meaningful. In business, the news industry, academic research, and many other fields, text summarization helps make quick decisions, access information faster, and manage resources more effectively. Additionally, text summarization research is conducted to further improve these technologies and develop new methods and algorithms to provide better summarization of texts. Therefore, text summarization and research in this field are of great importance in the information age. In this study, a new operating model for text summarization that can be applied to different algorithms is proposed and evaluated. Sixteen summarization algorithms covering six approaches (statistical, graph-based, content-based, pointer-based, position-based, and user-oriented) were implemented and tested on 50 different full-text article datasets. Four evaluation criteria (BLEU, Rouge-N, Rouge-L, METEOR) were used to assess the similarity between the generated summaries and the original summaries. The performance of the algorithms within each approach was averaged and the overall best-performing algorithm was selected. This best algorithm was subjected to further analysis through Topic Modelling and Keyword Extraction to identify key topics and keywords within the summarised text. The proposed model provides a standardized workflow for developing and thoroughly testing summarization algorithms across datasets and evaluation metrics to determine the most appropriate summarization approach. This study demonstrates the effectiveness of the model on a variety of algorithm types and text sources.

A Text Summarization Approach to Enhance Global and Local Information Awareness of Transformer

A Text Summarization Approach to Enhance Global and Local Information Awareness of Transformer

A Review on Optimization-Based Automatic Text Summarization Approach

A Review on Optimization-Based Automatic Text Summarization Approach

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

<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>

A novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods

Multi Document Summarization (MDS) is a technique for extracting succinct summaries from groups of related documents. The usage of MDS in the e-learning context is more appealing for providing summaries for learning materials, which helps students and teachers to focus on key concepts of the learning materials. In a learning material, availability of non-textual document-elements such as figures/diagrams, plots, graphs, tables and algorithms can be seen widely, but keeping such document-elements in the summary is not possible as they are non-textual. This proposed work incorporates the text summarization approach with document-elements augmentation to the summary to provide a detailed coverage of information without exceeding the summary length constraint. The key information in the source text is identified by important phrase features and sentence features, and the summaries are generated by selecting important sentences using the Integer Linear Programming (ILP) framework while reducing the redundancy using pre-trained sentence vectors. The relationships between the summary and document-elements are identified through document-element snippet extraction and a Hierarchical Agglomerative Clustering approach. Experimental results of the proposed summary extraction and augmentation on educational dataset (EduSumm) show better performance compared to the state-of-the-art approaches.

Automated Detection of Persuasive Content in Electronic News

Persuasive content in online news contains elements that aim to persuade its readers and may not necessarily include factual information. Since a news article only has some sentences that indicate persuasiveness, it would be quite challenging to differentiate news with or without the persuasive content. Recognizing persuasive sentences with a text summarization and classification approach is important to understand persuasive messages effectively. Text summarization identifies arguments and key points, while classification separates persuasive sentences based on the linguistic and semantic features used. Our proposed architecture includes text summarization approaches to shorten sentences without persuasive content and then using classifiers model to detect those with persuasive indication. In this paper, we compare the performance of latent semantic analysis (LSA) and TextRank in text summarization methods, the latter of which has outperformed in all trials, and also two classifiers of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). We have prepared a dataset (±1700 data and manually persuasiveness-labeled) consisting of news articles written in the Indonesian language collected from a nationwide electronic news portal. Comparative studies in our experimental results show that the TextRank–BERT–BiLSTM model achieved the highest accuracy of 95% in detecting persuasive news. The text summarization methods were able to generate detailed and precise summaries of the news articles and the deep learning models were able to effectively differentiate between persuasive news and real news.

EXABSUM: a new text summarization approach for generating extractive and abstractive summaries

Due to the exponential growth of online information, the ability to efficiently extract the most informative content and target specific information without extensive reading is becoming increasingly valuable to readers. In this paper, we present 'EXABSUM,' a novel approach to Automatic Text Summarization (ATS), capable of generating the two primary types of summaries: extractive and abstractive. We propose two distinct approaches: (1) an extractive technique (EXABSUMExtractive), which integrates statistical and semantic scoring methods to select and extract relevant, non-repetitive sentences from a text unit, and (2) an abstractive technique (EXABSUMive), which employs a word graph approach (including compression and fusion stages) and re-ranking based on keyphrases to generate abstractive summaries using the source document as an input. In the evaluation conducted on multi-domain benchmarks, EXABSUM outperformed extractive summarization methods and demonstrated competitiveness against abstractive baselines.

Extractive Summarization of Telugu Text Using Modified Text Rank and Maximum Marginal Relevance

With the rapid growth of digital content, there is a need for an automatic text summarizer to provide short text from a long text document. Many research works have been presented for extractive text summarization (ETS). This article mainly focuses on the graph-based ETS approach for multiple Telugu text documents. A modified Text-Rank algorithm is employed with the noun and verb count of each sentence in the text as the initial score of each node. To get the optimal features, a novel feature selection algorithm called improved Flamingo Search Algorithm is proposed in this article. Though graph-based ETS is an important approach, the generated summaries are redundant. To reduce the redundancy in the generated summary, maximum marginal relevance is combined with the modified Text-Rank. Different word-embedding techniques such as Fast-Text, Word2vec, TF-IDF, and one-hot encoding are utilized to experiment with the proposed approach. The performance of the proposed text summarization approach is evaluated with BLEU and ROUGE in terms of F-measure, precision, and recall.

Generating survey draft based on closeness of position distributions of key words

Automatically generating a survey draft is a challenge to text summarization research because it needs to select important sentences from important references in a large set of candidate papers for composing sections that are in line with section titles and different sections discuss the most relevant reference papers of different number, which are beyond the capability of previous text summarization approaches as they assume that all candidate papers should be included into one summary. This paper proposes an approach to generating survey draft according to a pattern consisting of sections with titles given by the user who requests the survey. The problem of generating each section can be divided into the following sub-problems: (1) rank the input scientific documents (in short documents) according to the title of a section, (2) determine the number of documents that are most relevant to the title, and (3) rank and select sentences from the selected documents according to the title. A position closeness distance of key word is proposed to rank a set of documents by measuring how closely two key words within section title are distributed within each document, which is used to rank the documents. The rationale is that the positions of the neighboring key words of a section title should be closer in more relevant documents than other words. As different sections have different number of selected documents, a method is proposed to determine the number of documents to be included into the current section based on the slope shape of the sorted rank curve of documents according to the section title. Based on the duality property of the closeness, ranks of sentences within a document can be directly obtained when the document is ranked according to the title of section, and both the importance and coherence of selected sentences can be reflected without extra calculation for ranking sentences. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with other approaches. The proposed approach is significant in applications as different surveys can be generated according to different patterns given by different users.

TxLASM: A novel language agnostic summarization model for text documents

In Natural Language Processing (NLP) domain, the majority of automatic text summarization approaches depend on a prior knowledge of the language and/or the domain of the text being summarized. Such approaches requires language dependent part-of-speech taggers, parsers, databases, pre-structured lexicons, etc. In this research, we propose a novel automatic text summarization model, Text Documents - Language Agnostic Summarization Model (TxLASM), which is able to perform extractive text summarization task in language/domain agnostic manner. TxLASM depends on specific characteristics of the major elements of the text being summarized rather than its domain, context, or language and thus rules out the need for language dependent pre-processing tools, taggers, parsers, lexicons or databases. Within TxLASM, we present a novel technique for encoding the shapes of major text elements (paragraphs, sentences, n-grams and words); moreover, we present language independent preprocessing algorithms to normalize words and perform relative stemming or lemmatization. Those algorithms and its Shape-Coding technique enable the TxLASM to extract intrinsic features of text elements and score them statistically, and subsequently extract a representative summary that is independent of the text language, domain and context. TxLASM was applied on an English and Portuguese benchmark datasets, and the results were compared to twelve state-of-the-art approaches presented in recent literature. In addition, the model was applied on French and Spanish news datasets, and the results were compared to those obtained by standard commercial summarization tools. TxLASM has outperformed all the SOTA approaches as well as the commercial tools in all four languages while maintaining its language and domain agnostic nature.

A Hybrid Text Summarization Approach Using Neural Networks and Metaheuristic Algorithms

A Hybrid Text Summarization Approach Using Neural Networks and Metaheuristic Algorithms

A Transformer based approach for Abstractive Text Summarization of Radiology Reports

Abstractive text summarization has emerged as a promising approach for generating conciseand informative summaries from radiology reports. The topic of research focuses on developing a mechanism for abstractive text summarization specifically tailored for radiology reports, to generate informative summaries from the voluminous and complex information contained within reports. Manual summarization is time-consuming and prone to errors, while automated techniques can save time, reduce human bias, and improve the overall quality of the generated summaries. The mechanism involves preprocessing the text with NLP techniques, utilizing deep learning-based architectures and transformer models, and generating summaries that capture the essence of the original reports in a more concise form.Challenges such as handling complex medical terminologies, incorporating contextual information, and evaluating the quality of generated summaries are important considerations in this mechanism. The potential applications of radiology report summarization include improving report readability, facilitatingdecision-making, and enabling large-scale data analytics. In this research, Biobart-V2 model is used for summarization and Rouge-L value of 69.42% is being achieved whereas the dataset used is MIMIC III.Further research is needed to address the remaining challenges in this domain and integrate summarization into clinical practice for more effective and efficient radiology report interpretation and patient care.

Retracted: XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Retracted: XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Centroid Based Clustering Approach for Extractive Text Summarization

Abstract: Extractive text summarization is the process of identifying the most important information from a large text and presenting it in a condensed form. One popular approach to this problem is the use of centroid-based clustering algorithms, which group together similar sentences based on their content and then select representative sentences from each cluster to form a summary. In this research, we present a centroid-based clustering algorithm for email summarization that combines the use of word embeddings with a clustering algorithm. We compare our algorithm to existing summarization techniques. Our results show that our approach stands close to existing methods in terms of summary quality, while also being computationally efficient. Overall, our work demonstrates the potential of centroid-based clustering algorithms for extractive text summarization and suggests avenues for further research in this area.

TEXT SUMMARIZATION IN MONGOLIAN LANGUAGE

Textual information in this new era, it is difficult to manually extract the summary of a large data different areas of social communication accumulates the enormous amounts of data. Therefore, it is important to develop methods for searching and absorbing relevant information, selecting important sentences, paragraphs from large texts, to summarize texts by finding topics of the text and frequency based clustering of sentences. In this paper, the author presents some ideas on using mathematical models in presenting the source text into a shorter version with semantics, graph-based approach for text summarization in Mongolian language.