News Text Summarization Based on Multi-Feature and Fuzzy Logic

There is an enormous amount of textual material, and it is only growing every single day. The data available on Internet comprised of Web pages, news articles, status updates, blogs which are unstructured. There is a great need to reduce much of these text data to shorter, focused summaries that capture the salient details so that the user can navigate it more effectively as well as check whether the larger documents contain the information that we are looking for. Text summary is generating a shorter version of the original text. The need of summarization arises because every time it is not possible to read the detailed document due to lack of time. Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online both to better help discover relevant information and to consume relevant information faster. To address the issue of time constraint, an extractive text summarization technique has been proposed in this research work which selects important sentences from a text document to get a gist of information contained in it. A fuzzy technique has been used to generate extractive summary from multiple documents by using eight and eleven feature sets. The eleven feature set combines the existing eight features (term frequency-inverse sentence, length of sentence in the document, location of sentence in document, similarity between sentences, numerical data, title overlap, subject object verb (SOV) qualifier, lexical similarity) and three ancillary features (proper nouns, hindi cue phrase, thematic words). It was seen that applying fuzzy technique with eleven features gave better results for summarization than the same using eight features. The precision increases in the range of 3–5% for different datasets. Datasets used were Hindi news articles from online sources.

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.

Text summarization is the process of creating a shorter version of one or more text documents. Automatic text summarization has become an important way of finding relevant information in large text libraries or in the Internet. Extractive text summarization techniques select entire sentences from documents according to some criteria to form a summary. Sentence scoring is the technique most used for extractive text summarization, today. Depending on the context, however, some techniques may yield better results than some others. This paper advocates the thesis that the quality of the summary obtained with combinations of sentence scoring methods depend on text subject. Such hypothesis is evaluated using three different contexts: news, blogs and articles. The results obtained show the validity of the hypothesis formulated and point at which techniques are more effective in each of those contexts studied.

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.

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.

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 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.

Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.

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.

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

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.

Recent advancements in technology have enabled the storage of voluminous data. As this data is abundant, there is a need to create summaries that would capture the relevant details of the original source. Since manual summarization is a very taxing process, researchers have been actively trying to automate this process using modern computers that could try to comprehend and generate natural human language. Automated text summarization has been one of the most researched areas in the realm of Natural Language Processing (NLP). Extractive and abstractive summarization are two of the most commonly used techniques for generating summaries. In this study, we present a new methodology that takes the aforementioned summarization techniques into consideration and based on the input, generates a summary that is seemingly better than that generated using a single approach. Further, we have made an attempt to provide this methodology as a service that is deployed on the internet and is remotely accessible from anywhere. This service provided is scalable, fully responsive, and configurable. Next, we also discuss the evaluation process through which we came up with the best model out of many candidate models. Lastly, we conclude by discussing the inferences that we gained out of this study and provide a brief insight into future directions that we could explore.

Existence of large amount of textual information available on the internet emerged serious research in the area of machine generated summarization. Manual summarization of these online text documents is a very difficult task for human beings. So we need an automatic text summarizer. Automatic Text Summarization (ATS) is “condensing the source text into a shorter version, while preserving its information content and overall meaning”. Even though the work of automatic text summarization started in 1950's, still it is lacking to achieve more coherent and meaningful summaries. The proposed approach provides automatic feature based extractive heading wise text summarizer to improve the coherence thereby improving the understandability of the summary text. It summarizes the given input document using local scoring and local ranking that is it provides heading wise summary. Headings of a document give contextual information and permit visual scanning of the document to find the search contents. The proposed approach applies the same features to all document sentences. But it ranks the sentences heading wise and selects top n sentences from each heading where n depends upon compression ratio. The final heading wise summary produced by this approach is a collection of summary of individual headings. Since the heading wise summary contains the equal proportion of sentences from each heading, it reduces the coherent gap of the summary text. Also it improves the overall meaning and understanding of the summary text. The outcomes of the experiment clearly show that heading wise summarizer provides better precision, recall and f-measure over the main summarizer, Ms-word summarizer, free summarizer and Auto summarizer.

Text summarization is a challenging task in the field of Natural Language Processing. In the case of lower resource language like Bengali is also a difficult task to make an automatic text summarization system. This paper is based on the extractive summarization of Bengali text. Text summarization deletes the less useful information in a piece of text or a paragraph and summarizes it into a confined text. This helps in finding the required text more effectively and quickly. There are many types of algorithms used for summarizing the text. Here in this paper, we have used TF-IDF and BERT-SUM technologies for the Bengali extractive text summarization.

The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language’s morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic representations, the lack of using rouge metrics in abstractive text summarization, and using recent deep learning models to adopt them in Arabic summarization studies is an essential demand.