Ve Approach Research Articles

Prediction of Certain examinations on AI procedures for Text rundown

Making text synopses from huge volume of unstructured text like client surveys, web log posts and online entertainment posts is a significant assignment in text mining applications. These synopses uncover the valuable data which depicts the whole report or surveys. Rundown errand could be performed utilizing two significant methodologies: Extractive and abstractive methodology. Extractive methodology distinguishes the huge piece of the report that uncovered the whole happy and removes it to shape synopsis. Abstractive approach makes synopsis in light of catchphrases and semantics from the archive, which makes it troublesome when contrasted with the other methodology. The extractive synopsis task is perplexing because of overt repetitiveness, enormous volume of text, changeability and semantics of regular language in the text. The wide pertinence as well as trying nature of the errand has enlivened dynamic research in the space by both scholar and industry specialists. This exploration centers around the plan of AI based frameworks for two center regions connected with text mining, specifically, Component based text synopsis and text comparability location. Existing element positioning and outline frameworks utilize an assortment of strategies including inactive semantic ordering; Innocent Bayes' and other semantics based approaches. Because of the intricacy of the undertaking there is a requirement for creating proficient frameworks. In this proposal, an element positioning framework in light of client inclinations have been created. Three different AI approaches have been embraced for highlight based miniature level extractive text synopsis development.

Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.

Automatic multi-documents text summarization by a large-scale sparse multi-objective optimization algorithm

AbstractDue 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.

Abstractive text summarization of low-resourced languages using deep learning.

Humans must be able to cope with the huge amounts of information produced by the information technology revolution. As a result, automatic text summarization is being employed in a range of industries to assist individuals in identifying the most important information. For text summarization, two approaches are mainly considered: text summarization by the extractive and abstractive methods. The extractive summarisation approach selects chunks of sentences like source documents, while the abstractive approach can generate a summary based on mined keywords. For low-resourced languages, e.g., Urdu, extractive summarization uses various models and algorithms. However, the study of abstractive summarization in Urdu is still a challenging task. Because there are so many literary works in Urdu, producing abstractive summaries demands extensive research. This article proposed a deep learning model for the Urdu language by using the Urdu 1 Million news dataset and compared its performance with the two widely used methods based on machine learning, such as support vector machine (SVM) and logistic regression (LR). The results show that the suggested deep learning model performs better than the other two approaches. The summaries produced by extractive summaries are processed using the encoder-decoder paradigm to create an abstractive summary. With the help of Urdu language specialists, the system-generated summaries were validated, showing the proposed model's improvement and accuracy.

Learning to Generate Popular Headlines

Headlines are not only essential for summarizing news articles but also for grabbing users’ attention. Headline generation is a type of text summarization that can employ either an extractive or abstractive approach, with the latter being more prevalent through deep learning models. However, creating a popular headline that can capture readers’ attention is challenging. To address this issue, we propose a hybrid headline generation approach that utilizes state-of-the-art transformer models to generate several headline variations for an article. Additionally, we use a model for predicting headline popularity that can choose the most popular headline from the generated ones. We also create a new dataset for predicting headline popularity by scraping Twitter accounts of news media. Our evaluation shows that fine-tuning summarization models for the headline generation task can significantly improve their performance. We also demonstrate that our proposed method can generate more popular headlines compared to the baseline methods that do not incorporate popularity prediction. For such an evaluation purpose, we create a popularity benchmark to automatically assess the effectiveness of our proposed headline generation approach in generating popular headlines.

Neural Attention Model for Abstractive Text Summarization Using Linguistic Feature Space

Summarization generates a brief and concise summary which portrays the main idea of the source text. There are two forms of summarization: abstractive and extractive. Extractive summarization chooses important sentences from the text to form a summary whereas abstractive summarization paraphrase using advanced and nearer-to human explanation by adding novel words or phrases. For a human annotator, producing summary of a document is time consuming and expensive because it requires going through the long document and composing a short summary. An automatic feature-rich model for text summarization is proposed that can reduce the amount of labor and produce a quick summary by using both extractive and abstractive approach. A feature-rich extractor highlights the important sentences in the text and linguistic characteristics are used to enhance results. The extracted summary is then fed to an abstracter to further provide information using features such as named entity tags, part of speech tags and term weights. Furthermore, a loss function is introduced to normalize the inconsistency between word-level and sentence-level attentions. The proposed two-staged network achieved a ROUGE score of 37.76% on the benchmark CNN/DailyMail dataset, outperforming the earlier work. Human evaluation is also conducted to measure the comprehensiveness, conciseness and informativeness of the generated summary.

TEXT SUMMARIZATION USING MACHINE LEARNING AND DEEP NEURAL NETWORK

The creation of summaries and their evaluation have grown in popularity over time. The two primary techniques for creating summaries from a given text corpus are extractive text summarization and abstractive text summarization. To create abstractive and extractive summaries, a variety of techniques and methodologies are available. Using machine learning and deep neural networks, an automatic extractive and abstractive text summarization technique is developed. To produce a summary from a given text, the Extractive model utilizes (Spacy, TF-IDF Vectorizer, and Latent semantic Analysis algorithm (LSA)) and the Abstract model employs (Encoder-decoder RNN with LSTM units and attention mechanism). The goal of the model is to demonstrate the benefits of applying the Extractive and Abstractive approach to text summarization in practical settings.

Prediction of Certain examinations on AI procedures for Text rundown

Making text synopses from huge volume of unstructured text like client surveys, web log posts and online entertainment posts is a significant assignment in text mining applications. These synopses uncover the valuable data which depicts the whole report or surveys. Rundown errand could be performed utilizing two significant methodologies: Extractive and abstractive methodology. Extractive methodology distinguishes the huge piece of the report that uncovered the whole happy and removes it to shape synopsis. Abstractive approach makes synopsis in light of catchphrases and semantics from the archive, which makes it troublesome when contrasted with the other methodology. The extractive synopsis task is perplexing because of overt repetitiveness, enormous volume of text, changeability and semantics of regular language in the text. The wide pertinence as well as trying nature of the errand has enlivened dynamic research in the space by both scholar and industry specialists. This exploration centers around the plan of AI based frameworks for two center regions connected with text mining, specifically, Component based text synopsis and text comparability location. Existing element positioning and outline frameworks utilize an assortment of strategies including inactive semantic ordering; Innocent Bayes' and other semantics based approaches. Because of the intricacy of the undertaking there is a requirement for creating proficient frameworks. In this proposal, an element positioning framework in light of client inclinations have been created. Three different AI approaches have been embraced for highlight based miniature level extractive text synopsis development.

The Design of Automatic Summarization of Indonesian Texts Using a Hybrid Approach

This study aims to design a model for automatic text summarization in Indonesian. Automatic text summarization is a system that reduces the number of sentences without losing important information in the document. There are three approaches in making automatic text summarization, namely: extractive, abstractive, and hybrid. The extractive approach is to select a core sentence without changing it to a new sentence. The abstractive approach is to construct new sentences that describe the contents of the document. Meanwhile, the hybrid approach designed in this study is a combination of extractive and abstractive approaches. By designing automatic text summarization in Indonesian with a hybrid approach, it is hoped that the results issued by the system will be more like man-made summaries and have higher readability. The design made is specifically for the input of a document. The stages of automatic text summarization are divided into two, namely preprocessing and process. In the Preprocessing stage, sentence separation, tokenization, coreference resolution, stop words removal, feature extraction are carried out. There are two stages of making the summary, namely the extraction stage, to select important sentences and the abstraction stage, to select words and arrange them into summary sentences. Further research can be carried out for input of longer documents and the input is in the form of multi documents

Extractive Text and Video Summarization using TF-IDF Algorithm

Abstract: Text summarization is a technique for extracting concise summaries from a large text without sacrificing any important information. It's a good way to extract crucial information from documents. The rapid rise of the internet has resulted in a substantial surge in data all across the world. It has become difficult for humans to manually summarise big documents. Automatic Text Summarization is an NLP technique that lowers the time and efforts required by a human to create a summary. Text summarising techniques are divided into two categories: extractive and abstractive. In the extractive approach, text summarising techniques choose sentences from documents based on a set of criteria. In the abstractive approach, text summarising techniques strive to improve sentence coherence by reducing redundancies and explaining the context of sentences. The extractive summarization approach is the subject of this paper. There are several methods for summarising data, including TF-IDF, Text Rank, PageRank, and Latent Dirichlet Allocation (LDA). This work examines Text Summarization using the TFIDF Algorithm, a numerical measure that ranks the value of a word in a document based on how frequently it appears in that document and a set of documents. The application of the TF-IDF Algorithm for text, document, article, and video summarization is described in this study. There are no repetitions in the results, and for some searches, they are nearly identical to the summary results provided by humans. This algorithm offers a sentence extraction technique that selects the most diverse top-ranked sentences. Keywords: Extractive Summarization, Term Frequency-Inverse Document Frequency (TF-IDF), Natural Language Processing (NLP), Text Summarization, Video Summarization

Modeling and Simulation of a Packed Column Batch Still for Fruit Wine Distillations

Batch distillations are extensively used worldwide to produce fruit wine spirits with distinctive aromas. These processes are typically operated manually, based on the experience of the distiller. However, dynamic optimization and automatic control strategies could be valuable for ensuring a more consistent product and quickly adapting to new market tendencies. Consequently, reliable process models are required for the application of these methods. The novelty of this study is developing a highly efficient method to reliably simulate and calibrate a rigorous first-principles model of a packed batch column still. We applied the method of lines (MOL) to transform the partial differential equations describing the packed column dynamics into an ordinary differential equation (ODE) system. The nonlinear phase equilibrium equations were pre-solved, and several polynomials were fitted to get explicit algebraic equations. The final differential-algebraic system (DAE) comprises 31 ODEs, 113 explicit algebraic equations, and two implicit algebraic equations. Variables scaling, smoothing of discontinuities, and applying an algebraic loop within Simulink to solve the implicit algebraic equations resulted in 600 times faster simulations than a naïve approach. The model was regressed using pilot-scale experimental data and subjected to extensive validation using independent data. Sensitivity and residual analysis established that a better heat loss model could improve ethanol concentration prediction. This new heat losses model reduced the average ethanol concentration error by more than five times. The developed model can be applied to design reliable control systems and optimal operating strategies for packed column batch distillations.

Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension

How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited supervision (only 2k instances) while maintaining sufficiency.

The Use of Transformer Model in Opinion Summarisation

We live in an age of information, therefore collected data and documentation are practically treasure resources. All about a business and its development can be estimated with clarity via statistics. Any machine that could really analyse information to predict a projected outcome is known for being extremely vital for the business. It is critical for the system to provide accurate and useful knowledge of the products in order to conduct accurate assessment. Summarisation is a technique for obtaining a rundown from series of sentences in a study or observation that facilitates us with understanding the basic content of the knowledge expressed within. Simple and brief summaries of just a product will assist the system in performing prospective product research and development. In our paper, we use a deep learning framework that provides to extract clean, relevant, brief summaries from comprehensive customer feedback. Strategies of abstractive text summarisation is used. The method of extracting the primary keyword from a statement and using them in the summary is defined as extractive text summarisation. We utilise abstractive summarisation in this case, which evolves from sample information and provides the best feasible description. Utilising Transformer with Depth Scaling MultiHeaded Attention as well as GloVe word embedding with positional encoding, we illustrate an abstractive approach to extract summaries from the Amazon fine food reviews dataset. Transformer aids in the parallelisation of workloads in order to process data more quickly. We have used an Attention layer which boost the model's quality and enables it to become more effective. The BLUE rating is used to quantify the model's potency.

Recent Advances in Arabic Automatic Text Summarization

Recently, the volume of the Arabic texts and documents on the internet had increased rabidly and generated a rich and valuable content on the www. Several parties had contributed to this content, this includes researchers, companies, governmental agencies, educational institutions, etc. With this big content it became difficult to search and extract useful information using only mankind skills and search engines. This motivated researchers to propose automated methodologies to extract summaries or useful information from those documents. A lot of research has been proposed for the automatic extraction of summaries for the English language and other languages. Unfortunately, the research for the Arabic automatic text summarization is still humble and needs more attention. This study presents a critical review and analysis of recent studies in Arabic automatic text summarization. The review includes all recent studies used the different text summarization approaches which include statistical-based, graph-based, evolutionary-based, and machine learning-based approaches. The selection criteria of the literature are based on the venue of publication and year of publication; back to five years. All review papers in Arabic ATS are excluded from the review since the study considers the recent methodologies in Arabic ATS. As a conclusion of this research, we recommend researchers in Arabic text summarization to investigate the use of machine learning on abstractive approach for text summarization due to the lack of research in this area. Keywords: Automatic Text Summarization, The Arabic Language, Machine Learning, Natural Language Processing, Text Processing, Computational Linguistics.

Subspace exploration: Bounds on Projected Frequency Estimation.

Given an n × d dimensional dataset A, a projection query specifies a subset C ⊆ [d] of columns which yields a new n × |C| array. We study the space complexity of computing data analysis functions over such subspaces, including heavy hitters and norms, when the subspaces are revealed only after observing the data. We show that this important class of problems is typically hard: for many problems, we show 2Ω(d) lower bounds. However, we present upper bounds which demonstrate space dependency better than 2 d . That is, for c, c' ∈ (0, 1) and a parameter N = 2 d an Nc -approximation can be obtained in space , showing that it is possible to improve on the naïve approach of keeping information for all 2 d subsets of d columns. Our results are based on careful constructions of instances using coding theory and novel combinatorial reductions that exhibit such space-approximation tradeoffs.

Narzędzia do automatycznego streszczania tekstów w języku polskim. Stan badań naukowych i prac wdrożeniowych

The goal of the publication is to present the state of research and works carried out in Poland on the issue of automatic text summarization. The author describes principal theoretical and methodological issues related to automatic summary generation followed by the outline of the selected works on the automatic abstracting of Polish texts. The author also provides three examples of IT tools that generate summaries of texts in Polish (Summarize, Resoomer, and NICOLAS) and their characteristics derived from the conducted experiment, which included quality assessment of generated summaries using ROUGE-N metrics. The results of both actions showed a deficiency of tools allowing to automatically create summaries of Polish texts, especially in the abstractive approach. Most of the proposed solutions are based on the extractive method, which uses parts of the original text to create its abstract. There is also a shortage of tools generating one common summary of many text documents and specialized tools generating summaries of documents related to specific subject areas. Moreover, it is necessary to intensify works on creating the corpora of Polish-language text summaries, which the computer scientists could apply to evaluate their newly developed tools.

Management Control in Cameroon Municipal Areas: A Response to the Urgent Issue of Risk Management

Abstract
 Generally, Cameroon municipalies encounter organisaonal risks. This research has idened a
 typology of such risks and presents management control as a cursor of enlightenment and
 governance in controlled risk. This objecve is achieved aer using a quantave method through a
 hypotheco-deducve approach aimed at tesng three hypotheses; the study was conducted using a
 quesonnaire administered in 9 out of 10 regions in Cameroon. At the end, this study highlights that
 management control in Cameroon municipal areas is void of proacve tools, and uses only tradional
 tools. It also highlights that ways of implemenng management control in Cameroon municipalies
 are not conducive to reduce risks.
 Key words: control management, organisaonal risks, municipality.

Narzędzia do automatycznego streszczania tekstów w języku polskim. Stan badań naukowych i prac wdrożeniowych

The goal of the publication is to present the state of research and works carried out in Poland on the issue of automatic text summarization. The author describes principal theoretical and methodological issues related to automatic summary generation followed by the outline of the selected works on the automatic abstracting of Polish texts. The author also provides three examples of IT tools that generate summaries of texts in Polish (Summarize, Resoomer, and NICOLAS) and their characteristics derived from the conducted experiment, which included quality assessment of generated summaries using ROUGE-N metrics. The results of both actions showed a deficiency of tools allowing to automatically create summaries of Polish texts, especially in the abstractive approach. Most of the proposed solutions are based on the extractive method, which uses parts of the original text to create its abstract. There is also a shortage of tools generating one common summary of many text documents and specialized tools generating summaries of documents related to specific subject areas. Moreover, it is necessary to intensify works on creating the corpora of Polish-language text summaries, which the computer scientists could apply to evaluate their newly developed tools.

Cassirer and Goldstein on Abstraction and the Autonomy of Biology

This article examines the mutual influence between Ernst Cassirer and his cousin, the neurologist Kurt Goldstein. For both Cassirer and Goldstein, views on the nature of human cognition were fundamental to their understanding of scientific knowledge, and these were informed by both philosophical theorizing and empirical research on pathologies of the nervous system. Following Cassirer, and in agreement with the physicalism of the Vienna Circle, Goldstein held that the physical sciences had progressed by arriving at abstract, mathematical representations to take the place of qualitative characterizations of observable reality. In tension with physicalism, Goldstein was not sanguine about the fruitfulness of the abstractive approach in biology. He proposed that biology must adhere to its own sui generis methods of observation and experimentation in order to obtain knowledge of the “natures” of living organisms. I argue that there is a parallel with Cassirer’s assertion of the differences between physical and cultural sciences, underwritten by the deployment of varying symbolic functions. I also propose that the neurological writings of Goldstein are an important backdrop to Cassirer’s positive evaluation of abstract thought, in contrast to the pessimism regarding a worldview dominated by scientific abstractions expressed by philosophers such as Bergson, Whitehead, and Husserl.

Comparison of Graph-based and Term Weighting Method for Automatic Summarization of Online News

Text summarization is one of the quickest ways to get the gist of a paragraph or story. In text summarization, there are two ways that can be used: extractive approach and abstractive approach. In this research, the summarization was conducted using extractive approach. The extraction process was conducted by taking a few sentences from a document and combining them into a short summary. The most common method used in conducting text summarization is graph-based method. The authors proposed another method for summarization, namely term weighting method. The purpose of this study is to compare between the result of graph-based method and term weighting method in order to determine the best method for text summarization. The text pre-processing phase involves omitting the stopwords and the affixes. Moreover, the researcher utilized the measurement of Precision, Recall and F-Score. Based on the experiment using the proposed method (term weighting method), the result shows that the average values on Precision and F-score for term-weighting method are 0.296 and 0.280 respectively, which are better than the values of graph-based method. In the end, the result shows that the proposed method, which is the term weighting method, produced better summary compared to graph-based method.