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Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents, in an effort to utilize a semantic representation of the unstructured textual content in a machine-readable, structured, and concise manner. The overall framework is based on a well defined methodology for performing semantic graph parsing, graph construction, graph transformations for machine learning models and deep learning predictions. The employed semantic graph representation focuses on using the model of abstract meaning representation. Several combinations of graph construction and graph transformation methods are investigated to specify the most efficient of them for the machine learning models. Additionally, a range of deep learning architectures is examined, including a sequence-to-sequence attentive network, reinforcement learning, transformer-based architectures, and pre-trained neural language models. In this direction, a semantic graph representation of an original text is extracted, and then the present framework formulates the problem as a graph-to-summary learning problem to predict a summary of an original text. To the best of our knowledge, this formulation of graph-to-summary predictions in abstractive text summarization, without other intermediate steps in the machine learning phase, has not been presented in the relevant literature. Another important contribution is an introduction of a measure for assessing the factual consistency of the generated summaries in an effort to provide a qualitative evaluation. To assess the framework, an extensive experimental procedure is presented that uses popular datasets to evaluate key aspects of the proposed approach. The obtained results exhibit promising performance, validating the robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a lucrative field in natural language processing (NLP). The amount of data flow has multiplied with the switch to digital. The massive datasets hold a wealth of knowledge and information must be extracted to be useful. This article focusses on creating an unmanned text summarizing structure that accepts text as data feeded into the system to outputs a summary using a cutting-edge machine learning model. Advancements in NLP led to the introduction of transformers in the field and their outstanding performance pulled a lot of attention towards them. The two transformer-based language models namely, Bidirectional and Auto-regressive Transformer (BART) and Text-To-Text Transfer Transformer (T5) were implemented on the CNN_dailymail dataset. BART outperforms T5 by 3.02% in ROUGE-1 Score. The model provides a worthier performance in comparison to the other models introduced in the existing literature for performing the same task. [ABSTRACT FROM AUTHOR]

The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. [ABSTRACT FROM AUTHOR]

A document known as notitia criminis (NC) is use in the Brazilian Federal Police as the starting point of the criminal investigation. An NC aims to report a summary of investigative activities. Thus, it contains all relevant information about a supposed crime that occurred. To manage an inquiry and correlate similar investigations, the Federal Police usually needs to extract essential information from an NC document. The manual extraction (reading and understanding the entire content) may be human mentally exhausting, due to the size and complexity of the documents. In this light, natural language processing (NLP) techniques are commonly used for automatic information extraction from textual documents. Deep neural networks are successfully apply to many different NLP tasks. A neural network model that leveraged the results in a wide range of NLP tasks was the BERT model—an acronym for Bidirectional Encoder Representations from Transformers. In this article, we propose approaches based on the BERT model to extract relevant information from textual documents using automatic text summarization techniques. In other words, we aim to analyze the feasibility of using the BERT model to extract and synthesize the most essential information of an NC document. We evaluate the performance of the proposed approaches using two real-world datasets: the Federal Police dataset (a private domain dataset) and the Brazilian WikiHow dataset (a public domain dataset). Experimental results using different variants of the ROUGE metric show that our approaches can significantly increase extractive text summarization effectiveness without sacrificing efficiency. [ABSTRACT FROM AUTHOR]

Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics. [ABSTRACT FROM AUTHOR]