A Comprehensive Review of Arabic Text Summarization

Leveraging Transformer Summarizer to Extract Sentences for Arabic Text Summarization

Nowadays, textual information in numerical format is hugely produced, which requires the development of tools such as Automatic Text Summarization to produce a condensed and relevant representation, thus helping the reader to decide whether the source document contains the information they are looking for or not. The Text Summarization has known these last decades an enormous progress, in particular works of Arabic language. The objective of this paper is to contribute more to these advances by proposing an Arabic text summarization tool (SumSAT), which adopts an extraction approach using hybridization between three techniques which are: Contextual exploration, indicative expression, and the graph method. Experimental results showed that the proposed approach achieved competitive and promising in the optic of generating a small and coherent summary.

How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

Text summarization techniques offer a way to address the significant challenges faced by clinicians and researchers due to the exponential growth of information in healthcare on the internet. By condensing lengthy text into concise summaries, these techniques facilitate faster, easier, and convenient access to relevant information. This is particularly beneficial in use cases such as online user feedback/reviews about drugs, where valuable insights can be obtained that extend beyond clinical trials and observational studies. This paper comprehensively evaluates six widely used text summarization techniques (LSA, Luhn’s Method, Text Rank, T5 Transformer, and Kullback-Leibler, BERT) in extracting key insights, themes and patterns about drugs from online drug reviews. The evaluation considers both quantitative and qualitative aspects, focusing on their applicability to the challenging medical terminology, which is known for its inherent intricacies and complexities. The findings of this study showed the performance of text summarization techniques using metrics such as F1 score, Recall, and Precision, focused on the unigram, bigram, and trigram overlap between the generated text summaries and the reference summaries, utilizing the ROUGE-1, ROUGE-2, and ROUGE-L evaluation methods. It is shown that results showed TextRank to be the most effective text summarization method followed by BERT when working with Medical Terminology in Healthcare & Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms.

Usage of online textual media is steadily increasing. Daily, more and more news stories, blog posts and scientific articles are added to the online volumes. These are all freely accessible and have been employed extensively in multiple research areas, e.g. automatic text summarization, information retrieval, information extraction, etc. Meanwhile, online debate forums have recently become popular, but have remained largely unexplored. For this reason, there are no sufficient resources of annotated debate data available for conducting research in this genre. In this paper, we collected and annotated debate data for an automatic summarization task. Similar to extractive gold standard summary generation our data contains sentences worthy to include into a summary. Five human annotators performed this task. Inter-annotator agreement, based on semantic similarity, is 36% for Cohen’s kappa and 48% for Krippendorff’s alpha. Moreover, we also implement an extractive summarization system for online debates and discuss prominent features for the task of summarizing online debate data automatically.

Proper nouns are usually primary keys in a query. Their correct translation might be necessary to maintain a good retrieval performance in a cross language information retrieval (CLIR) system. However, dictionaries only include the most commonly used proper nouns, like major countries and capitals. As they are spelling variants of each other in most languages, using an approximate string matching technique against the target database index is the common approach taken to find the target language correspondents of the original query key. N-gram technique proved to be the most effective among other approximate string matching techniques. As we are dealing with an English-Arabic CLIR system which involves two languages of different alphabets, we decided to combine transliteration with the n-gram technique to generate the different spelling variants of out of vocabulary (OOV) words. We call this technique: Transliteration Ngram (TNG). One issue that arises with the Arabic language is that words that are spelled similarly can have different meanings depending on the context of the sentence. This is particularly true for proper names, which usually have a meaning if used as a verb or adjective. To further enhance our transliteration approach, we chose to use part of speech (POS) disambiguation to reduce the number of unrelated words from the set transliterations obtained using TNG.

Abstractive summarization has become the mainstream of automatic text summarization because of its unique flexibility. Pointer-Generator Network(PGN) has already been the de facto standard for the text summarization model. However, this model still has two main shortcomings: first, since the word embeddings obtained from the word embedding matrix lack of contextual semantic information, the model tends to ignore the global information of the input text when generating the summary. Second, the embedding matrix is only learned on a single corpus from scratch, which decreases the convergence speed of the PGN model and affects the generalization ability of the word embeddings. In this paper, we propose a new text summarization framework, Two-Stage Encoder for pointer-generator network with Pretrained Embeddings, which aims to solve the problems mentioned above. We introduce an encoder based on the multi-head self-attention for the first-stage encoding. This encoder module can improve the word representation by solving the polysemy problem in the PGN model and alleviate the problem of ignoring global information. Besides, we apply the pretrained word embeddings GloVe to initialize the word embedding matrix and then fine-tune it. A range of experiments are conducted on the CNN/Daily Mail dataset. For the CNN/Daily mail dataset, comparing with the baseline model, the proposed model enhances the ROUGE metrics by 0.75, 0.74 and 0.77 respectively.

Text Summarization as a phenomenon has always been present and rather an evolving one with the advent of new technologies both in terms of data collection as well for the processing of this data. One reason of using text summarization is the huge amount of data floating over the internet in the form of text files, comments which is though potent enough to be used to extract useful information. but since the amount of text present in these sources is too huge, so the need of text summarization becomes justified by every argument. Some of the areas where text summarization is vastly used is applications involved in providing capsule information such as compact news applications, or websites providing academic notes for various examinations This paper presents an auto text summarizer application which takes the URL of a web page as input, performs summarization on the selected elements and then presents this summarized text content on the front end of a web application. At the backend, the process of scraping of web page content (if an http URL is provided as input) using beautiful soup library or reading of text provided takes place. news in short forms, or micro blogging websites. The scraped content after being preprocessed properly is summarized using a suitable library which in our case is one among NLTK, Spacy, Genism and Sumy. The summarized content is presented at the frontend using flask framework of Python. The results produced using different libraries are compared in the end in terms of reading time of the summarized content. The application uses extractive text summarization technique in order to achieve its result which is a compact summary of the textual data prepared from the keywords already present in the document.

Text summarization is referred to the process of rewriting a particular content into its brief version by understanding it. It is a serious need to obtain concise and relevant information from huge information. Automatic text summarization is fast-growing research area in natural language processing from last few years. It has improved from simple heuristics to neural network-based text summarization techniques. The extractive and abstractive text summarization generate the condensed text which saves time in reading the entire document. The abstractive approach is more complex as it needs natural language processing and neural network. Due to the availability of huge unsupervised data, traditional approach fails to provide accuracy in text summarization. The deep learning-based text summarization model gives a good performance as compared to the conventional techniques. In this paper, we have reviewed the recent work on text summarization based on deep learning techniques.

Summarization of Text is extracting important information from a body of text and present it in the form of a concise summary. The need for summarization has increased in recent times. The importance of having a simple concise summary of information in the news, business and research domains is paramount. Automatic text summarization is a well known task in the NLP (Natural Language Processing) field. Text Summarization techniques can be large divided into two groups: Extractive summarization and Abstractive summarization. Extractive summarization is based on identifying key sentences or phrases from the source text and grouping them to produce a summary without rewriting or paraphrasing the original text. Abstractive summarization is based on utilizing a deeper understanding of the source text and generating new sentences, not present in the original text, which improves the summary by reducing redundancy and focusing on the meaning of the source text. In this study we implement and compare the performance of various automatic summarization methods in order to gain insight into how long the methods take to implement and how accurate and human-like the generated summaries are. We aim to learn the pros and cons of the various techniques used by utilizing summary scoring as well as manual inspection of generated summary.

SASHAKT is a job portal designed specifically for women, utilizing text extraction and summarization techniques to provide a user-friendly and efficient job search experience. The portal extracts relevant information from job postings and summarizes it, allowing women to quickly identify job opportunities that align with their skills and qualifications. Additionally, the portal utilizes text classification algorithms to identify and filter out job postings that may be discriminatory or biased toward women. This study presents the development and implementation of SASHAKT, including a detailed description of the text extraction and summarization techniques used and the text classification algorithms implemented to detect discriminatory language. The study also presents the results of user testing and evaluations of SASHAKT, highlighting its effectiveness in improving the job search experience for women. The results of this study demonstrate that SASHAKT can help increase women's representation in the workforce by providing them with a more efficient way to find job opportunities that align with their skills and qualifications. Furthermore, the study also highlights the potential for similar text-based approaches to be applied to other areas of job search and career development for underrepresented groups such as people with disabilities and minority groups. Overall, the study concludes that SASHAKT is an innovative solution that addresses the need for a more inclusive job search experience for women by utilizing natural language processing techniques.

In graph-based extractive text summarization techniques, the weight assigned to the edges of the graph is the crucial parameter for the sentence ranking. The weights associated with the edges are based on the similarity between sentences (nodes). Most of the graph-based techniques use the common words based similarity measure to assign the weight. In this paper, we propose a new graph-based summarization technique, which, besides taking into account the similarity among the individual sentences, also considers the similarity between the sentences and the overall (input) document. While assigning the weight among the edges of the graph, we consider two attributes. The first attribute is the similarity among the nodes, which forms the edges of the graph. The second attribute is the weight given to a component that represents how much the particular edge is similar to the topics of the overall document for which we incorporate the topic modeling. Along with these modifications, we use the semantic measure to find the similarity among the nodes. The evaluation results of the proposed method demonstrate a significant improvement of the summary quality over the existing text summarization techniques.

The application of machine learning (ML) and natural language processing (NLP) is being extensively used for research in the area of healthcare and biomedicine. This pattern goes especially in accordance with the course, and the healthcare system is headed in the highly networked world which includes the World Wide Web where the regular users and health experts conduct discourses on health issues. To glean knowledge from medical texts and discourses which are mostly in text in natural language, many text analysis frameworks and techniques have been designed. Those techniques do not produce a comprehensive summary about the content related to a disease from content available online. So in our work, we propose text summarization based on natural language processing algorithms combined with machine learning algorithms for extracting all information pertaining to a disease from online healthcare forums.KeywordsText summarizationTextRankPageRankWeighted graphMachine learningNatural language processing

We present a method for open vocabulary retrieval based on a spoken document retrieval (SDR) system using subword models. The present paper proposes a new approach to open vocabulary SDR system using subword models which do not require subword recognition. Instead, subword sequences are obtained from the phone sequence outputted containing an out of vocabulary (OOV) word, a speech recognizer outputs a word sequence whose phone sequence is considered to be similar to the OOV word. When OOV words are provided in a query, the proposed system is able to retrieve the target section by comparing the phone sequences of the query and the word sequence generated by the speech recognizer.

The evolution of Artificial Intelligence (AI) gave insight into how humans should interact with machines. Natural Language Processing (NLP) is an area under AI, which aims to minimize the communication gap between human and machine. Text summarization is a sub-domain of NLP, which primarily focuses on summarizing text documents into a meaningful summary. Text summarization is useful in the areas, such as media monitoring, search engine optimization (SEO), question answering systems, etc. Lots of research works are carried out in the area of text summarization. A majority of the research works are adopted syntactic approaches. However, the semantic based approaches provide more meaningful summary compared to syntactic approaches. Due to the complexity of the semantic based methods, only few works are reported especially in Indian languages. In this paper, an overview of various semantic based text summarization methods are explored. An extensive review is done in languages, such as English, Arabic, Chinese, Hindi, Kannada, Tamil, and Malayalam. At the end of this survey, a critical analysis was done on various semantic based text summarization techniques. This analysis will not only give insights to potential prospective researchers, but also come up with an improved framework or method for semantic based abstractive text summarization.