A survey on Automatic Text SummariText Summarization endeavors to produce a summary version of a Text, while maintaining the original ideas. The Textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such a massive amount of data to extract useful information is a significant undertaking, and requires an automatic mechanism to aid with the extant repository of information. The Text Summarization systems intent to assist with content reduction keeping the relevant information and filtering the non-relevant parts of the Text. In terms of the input, there are two fundamental approaches among the Text Summarization systems. The first approach summarizes a single document. In other words, the system takes one document as an input and produces a summary version as its output. An alternative approach is to take several documents as its input and produce a single summary document as its output. In terms of output, the Summarization systems are also categorized into two major types. One approach would be to extract exact sentences from the original document to build the summary output. An alternative would be a more complex approach, in which the rendered Text is a rephrased version of the original document. This paper will offer an in-depth introduction to automatic Text Summarization. We also mention some evaluation techniques to evaluate the quality of automatic Text Summarization. zation

Due to the explosive growth of the world-wide web, automatic Text Summarization has become an essential tool for web users. In this pa- per, we present a novel approach for creating Text summaries. Using fuzzy logic and word-net, our model extracts the most relevant sentences from an original document. The approach utilizes fuzzy measures and inference on the extracted Textual information from the document to find the most significant sentences. Experimental results reveal that the proposed approach extracts the most relevant sentences when compared to other commercially available Text summarizers. Text pre-processing based on word-net and fuzzy analysis is the main part of our work.

Today, with rapid growth of the World Wide Web and creation of Internet sites and online Text resources, Text Summarization issue is highly attended by various researchers. Extractive-based Text Summarization is an important Summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extractive-based Text Summarization seems to be an unsolvable problem. Therefore, to deal with such problems, meta-heuristic techniques are applied as a solution. In this paper, we used Cuckoo Search Optimization Algorithm (CSOA) to improve performance of extractive-based Summarization method. The proposed approach is examined on Doc. 2002 standard documents and analyzed by Rouge evaluation software. The obtained results indicate better performance of proposed method compared with other similar techniques.

Text Summarization is the process of condensing a source Text while retaining its key points, tailored to a specific audience or task. The research extractive Summarization, where each news article was segmented into individual sentences. Each sentence underwent processing through the ParsBERT algorithm. Subsequently, an attention layer combined the sentence weights with the Bidirectional GRU algorithm's output to extract summarized sentences for labeling. The dataset comprised over 175,000 articles sourced from reputable Persian news agencies (ISNA-TASNIM), covering various topics such as science, politics, and sports. Evaluation of the Summarization techniques was conducted using Rouge metrics. The results of the investigation revealed precision values of 0.7923 (Rouge-1), 0.7613 (Rouge-2), and 0.8582 (Rouge-L). The study also evaluated the effectiveness of Gated Recurrent Unit (GRU) algorithms in extractive Summarization by integrating its architecture with the attention network. The results demonstrated an improvement in news Text Summarization compared to other deep learning hybrid algorithms.

The volume of digital Text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic Text Summarization. Many approaches and algorithms have been proposed for automatic Text Summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic Text Summarization models.