Automatic Text Summarization of Konkani Texts Using Latent Semantic Analysis

Automatic text summarization is a popular area in Natural Language Processing and Machine Learning. In this work, we adopt a graph-based text summarization approach, using PageRank algorithm, for automatically summarizing Konkani text documents. Konkani is an Indo-Aryan language spoken primarily in the state of Goa, which is on the west coast of India. It is a low-resource language with limited language processing tools. Such tools are readily available in other popular languages of choice for automatic text summarization, like English. The Konkani language dataset used for this purpose is based on Konkani folktales. We examine the impact of various language-independent and language-dependent similarity measures on the construction of the graph. The language-dependent similarity measures use pre-trained fastText word embeddings. A fully connected undirected graph is constructed for each document with the sentences represented as the graph's vertices. The vertices are connected to each other based on how strongly they are related to one another. Thereafter, PageRank algorithm is used for ranking the scores of the vertices. The top-ranking sentences are used to generate the summary. ROUGE toolkit was used for evaluating the quality of these system-generated summaries, and the performance was evaluated against human generated “gold-standard” abstracts and also compared with baselines and benchmark systems. The experimental results show that language-independent similarity measures performed well compared to language-dependent similarity measures despite not using language-specific tools, such as stop-words list, stemming, and word embeddings.

Automatic text summarization is the process of generating or extracting a brief representation of an input text. There are several algorithms for extractive summarization in the literature tested by using English and other languages datasets; however, only few extractive Arabic summarizers exist due to the lack of large collection in Arabic language. This paper proposes and assesses new extractive single-document summarization approaches based on analogical proportions which are statements of the form “a is to b as c is to d”. The goal is to study the capability of analogical proportions to represent the relationship between documents and their corresponding summaries. For this purpose, we suggest two algorithms to quantify the relevance/irrelevance of an extracted keyword from the input text, to build its summary. In the first algorithm, the analogical proportion representing this relationship is limited to check the existence/non-existence of the keyword in any document or summary in a binary way without considering keyword frequency in the text, whereas the analogical proportion of the second algorithm considers this frequency. We have assessed and compared these two algorithms with some language-independent summarizers (LexRank, TextRank, Luhn and LSA (Latent Semantic Analysis)) using our large corpus ANT (Arabic News Texts) and a small test collection EASC (Essex Arabic Summaries Corpus) by computing ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (BiLingual Evaluation Understudy) metrics. The best-achieved results are ROUGE-1 = 0.96 and BLEU-1 = 0.65 corresponding to educational documents from EASC collection which outperform the best LexRank algorithm. The proposed algorithms are also compared with three other Arabic extractive summarizers, using EASC collection, and show better results in terms of ROUGE-1 = 0.75 and BLEU-1 = 0.47 for the first algorithm, and ROUGE-1 = 0.74 and BLEU-1 = 0.49 for the second one. Experimental results show the interest of analogical proportions for text summarization. In particular, analogical summarizers significantly outperform three among four language-independent summarizers in the case of BLEU-1 for ANT collection and they are not significantly outperformed by any other summarizer in the case of EASC collection.

Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such a gigantic amount of textual content, human text summarization becomes impractical since it is expensive and consumes a lot of time and effort. So, developing automatic text summarization (ATS) systems is becoming increasingly essential. ATS approaches are either extractive or abstractive. The extractive approach is simpler and faster than the abstractive approach. This work proposes an extractive ATS system that aims to extract a small subset of sentences from a large multi-document text. First, the whole text is preprocessed by applying some natural language processing techniques such as sentences segmentation, words tokenization, removal of stop-words, and stemming to provide a structured representation of the original document collection. Based on this structured representation, the ATS problem is formulated as a multi-objective optimization (MOO) problem that optimizes the extracted summary to maintain the coverage of the main text content while avoiding redundant information. Secondly, an evolutionary sparse multi-objective algorithm is developed to solve the formulated large-scale MOO. The output of this algorithm is a set of non-dominated summaries (Pareto front). A novel criterion is proposed to select the target summary from the Pareto front. The proposed ATS system has been examined using (DUC) datasets, and the output summaries have been evaluated using (ROUGE) metrics and compared with the literature.

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.

Automatic text summarization is an emerging field of research in Natural Language Processing. This work is a novel attempt to include a low-resource language to the domain of Automatic Text Summarization. We use supervised machine learning algorithms to perform single document extractive automatic text summarization on documents in a low-resource language, Konkani. In particular, we propose using language independent features to train supervised machine learning algorithms using a Konkani dataset, specifically devised for the experimentation using books on Konkani folktale literature. We approach the automatic text summarization task as a binary classification problem, and the algorithms, once trained, classify the sentences based on their relevance to generate a summary. Thereafter, the performance of popular linear and non-linear supervised machine learning algorithms is evaluated using K-fold cross-validation. The summary generated by the systems is compared with human-generated summaries to verify its effectiveness. The results show that the linear models exhibit better performance in comparison with the non-linear models; however, all the models could beat the baselines. The output produced by the proposed methodology generates promising summaries without the need for any language-specific domain knowledge.

Text summarization is a technique for reducing lengthy passages of text into smaller portions. The goal is to develop a logical and fluent summary that only includes the document's major ideas. It's a important task in Natural Language Processing (NLP) that undoubtedly has a significant impact on synthesization of lengthy documents. With the rise of the digital documentation and publication, devoting time to thoughtfully read an article, document, or book in order to determine its relevance is no longer an option, especially given time constraints. In machine learning and NLP, automatic text summarization is a generic problem. A comparison of text summarization applying the Term Frequency ~ Inverse Document Frequency, Latent Semantic Analysis and Text Rank algorithms for the Malayalam language is presented in this research paper.

The aim of this study is to evaluate how effective the Lexrank algorithm and Latent semantic analysis (LSA) are in automatic text summarization for the Indonesian language. This research focuses on natural language processing and handling of excessive data. We applied both algorithms to generate text summaries using the INDOSUM dataset, which contains about 20,000 news articles in Indonesian with manual summaries. To assess performance, the ROUGE metric was used, which includes aspects of precision, recall, and F1 score. In all tested metrics, LSA outperformed Lexrank. LSA had a precision of 0.57, recall of 0.67, and an F1 score of 0.59, whereas Lexrank had a precision of 0.46, recall of 0.52, and an F1 score of 0.48. These result indicate that LSA is better at gathering important information from the original text than Lexrank.

Topic modeling is the statistical model for discovering hidden topics or keywords in a collection of documents. Topic modeling is also considered a probabilistic model for learning, analyzing, and discovering topics from the document collection. The most popular techniques for topic modeling are latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA), latent Dirichlet allocation (LDA), and the recent deep learning-based lda2vec. LDA is most commonly used in extractive multi-document summarization to determine whether the extracted sentence reflects the concept of the input document. In this paper, we will try to explore various multi-document summarization techniques that use LDA as a topic modeling method for improving final summary coverage and to reduce redundancy. Finally, we compared LDA and LSA using the Genism toolkit, and our experiment results show that LDA outperforms LSA if we increase the number of features considered for sentence selection.KeywordsTopic modelingText summarizationLDALSA

Increased availability of the Internet and social media has created another ‘world of data’ comprised of text, audio and video files. It is very difficult for a user to get the accurate summary or to comprehend the relevant and important items from the available media. Additionally, readers or evaluators of these data files are interested only in the relevant content or summary to be retrieved in the less duration from the source files. Automatic text summarization (ATS) is the only way to summarize single or multiple documents to obtain relevant content from the source files. Available ATS systems generate bad summaries and take a lot of time and space for long documents due to inaccurate encoding. Therefore, in this work, we have introduced an approach for extractive text summarization using sentence ranking. Experiments have been performed over BBC and CNN news datasets and evaluated in terms of ROUGE using N-gram Language Model. The quantitative values of the metrics show the effectiveness of the proposed approach for news datasets.

Automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the accuracy of sentence similarity calculations and subject delineation, and helps the abstracts generated to cover the documents comprehensively as well as reducing redundancies. The effectiveness of the method is proved by the experimental results. Compared with the traditional keyword-based vector space model method of automatic text summarization, the quality of the abstracts generated was significantly improved.

People sourcing for a particular job role in any corporate venture is a painstaking, progress-impeding task, especially given the changing job market trends, quality of candidate profile and the sheer number of applicants. This paper elucidates an improvised topic modelling approach, where the semantic analysis stage is prepended with text summarization, for the procedure of people sourcing for job roles using topic modelling and machine learning techniques. The distinction between choosing the perfect candidate for the job and choosing a good candidate who is adept in certain domains but not relevant for the job and getting the candidate up to speed by providing on job training, and the downtime involved in the process is certainly a deciding factor in hiring a potential employee. The following paper aims to alleviate this issue by describing the algorithm which identifies the most suitable candidate in the applicants’ pool through a novel, robust approach which uses topic modelling techniques like Latent Semantic Indexing (LSA) and Latent Dirichlet Allocation (LDA). It is empirically found that the precision of profile matching task was enhanced by using text summarization (through TextRank model) for both LSA and LDA by 21.4% and 50% respectively, and LSA outperforming LDA with regard to precision, in both with and without text summarization cases by 23.8% and 51.57%, respectively.

Leveraging Transformer Summarizer to Extract Sentences for Arabic Text Summarization

The method of reducing information from an original text document while maintaining the vital information is known as text summarizing. The amount of text data available has increased dramatically in recent years from a variety of sources. A large volume of text is an excellent source of information and knowledge of the source is essential for efficiently summarizing information that must be useful. Summarization facilitates the acquisition of vital and required information in a short period of time. Text summarization is required in a variety of domains, including news article summaries, email summaries and information summaries in the medical profession to track a patient's medical history for future treatment and so on. In summarization, there are two methods: extractive summarization and abstractive summarization. In this work, extractive summarization is used on the COVID-19 dataset. Different models and their results have been discussed.

Low Resource Languages are the languages that few people speak and the low-density languages in the world. Usually, more people speak languages such as English, Chinese, Hindi, French, Spanish, etc. Researchers are more comfortable with the above languages. because these languages have annotated data or dataset available online for applying technologies like Voice assistance, Text summarization, Google translation, Auto correction, etc. Data availability provides good training and testing of the model. Most of the NLP applications are developed with high-resource languages only. Despite the advancement of the digital era, few languages have low density. One of the challenges of implementing NLP applications for low-resource languages are not having data, even if parsing tools are not available. This paper addressed a survey on improving NLP in low-resource languages, applications, and challenges.

In this era of growing digital media, the volume of text data increases day by day from various sources and may contain entire documents, books, articles, etc. This amount of text is a source of information that may be insignificant, redundant, and sometimes may not carry any meaningful representation. Therefore, we require some techniques and tools that can automatically summarize the enormous amounts of text data and help us to decide whether they are useful or not. Text summarization is a process that generates a brief version of the document in the form of a meaningful summary. It can be classified into abstractive text summarization and extractive text summarization. Abstractive text summarization generates an abstract type of summary from the given document. In extractive text summarization, a summary is created from the given document that contains crucial sentences of the document. Many authors proposed various techniques for both types of text summarization. This paper presents a survey of extractive text summarization on graphical-based techniques. Specifically, it focuses on unsupervised and supervised techniques. This paper shows the recent works and advances on them and focuses on the strength and weaknesses of surveys of previous works in tabular form. At last, it concentrates on the evaluation measure techniques of summary.