Review of automatic text summarization techniques and methods

Widyassari et al. "Review of automatic text summarization techniques and methods". Journal of King Saud University – Computer and Information Sciences, pp 1029-1046, 2020. Accessed 9 September 2023, 2022, https://www.sciencedirect.com/journal/journal-of-king-saud-university-computer-and-information-sciences/vol/34/issue/4.

This 2020 Review of automatic text summarization techniques and methods introduces what automatic text summarization is and discusses the main approaches – namely: 'extractive and subtractive'. Extractive summarization selects and extracts existing content, while abstractive summarization generates new content to create summaries. 

The article provides an in-depth explanation of the topics and trends that were the focus of this field of research between 2008 and 2019. Specific notes are made about the "formidable challenge in the field of Natural Language Processing." (p. 1031). The research was conducted employing a 'Systemic Literature Review (SLR) methodology to analyze research trends, approaches, and challenges in the field of text summarization. Sources used were ScienceDirect, ieeexpore.ieee.org, and dl.acm.org. Data included novel datasets with language domains other than English (p. 1034).

A Text Mining Research Based on LDA Topic Modelling

Tong, Zhou, and Haiyi Zhang. "A Text Mining Research Based on LDA Topic Modelling". Computer Science & Information Technology, vol 6, 2016. https://www.researchgate.net/publication/303563965_A_Text_Mining_Research_Based_on_LDA_Topic_Modelling

This research paper explains the design of a new computational tool based on  topic models using text  mining techniques  to  organize, search and analyze the  vast  amounts of  data, providing a better way understanding and mining the information. The authors explain that, whereas typical text mining tasks include text categorization, text clustering, document summarization, keyword extraction etc. this research is focused on statistical and machine learning techniques for mining meaningful information and exploration of data analysis. They explain how Topic modelling methods are generally used for automatically organizing, understanding, searching, and summarizing large electronic archives; and that "Topics signifies the hidden, to be estimated, variable relations that link words in a vocabulary and their occurrence in documents".(p.202).

LDA is explained as "Latent Dirichlet allocation (LDA) a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar]. They state that "LDA has made a big impact in the fields of natural language processing and statistical machine learning and has quickly become one of the most popular probabilistic text modelling techniques in machine learning" (p.202).

The paper explains the designs and two experiments used to apply topic modeling so as to propose a new text mining solution on topics. "The first one is to use topic modelling manage and explore Wikipedia, and the second is a Twitter application on topic modelling. The former is a new solution on a typical problem and the latter is building u a new model on Twitter data analysis. (p.204)" The authors provide a brief introduction of each experiment including overview, pre-processing, and model training. With this data and model foundation, a number of future works are outlined that can be done for further research and experiment. (P. 209).

Google’s generative AI tool might give wildly inaccurate summaries

Stokel-Walker, Chris. "Google’s generative AI tool might give wildly inaccurate summaries". ‘Just because you have a generative AI hammer doesn’t make everything a nail,’ says one expert.' Fast Company, 17 August 2023. Accessed 1 October 2023, https://www.fastcompany.com/90940032/googles-generative-ai-summarization-tool-could-be-a-nightmare

This article discusses Google's introduction of an experimental tool named "SGE while browsing" that employs generative AI to summarize web content in real-time, aiming to enhance users' information consumption. It highlights concerns about the potential risks associated with generative AI summarization, including the likelihood of generating inaccurate or hallucinated content. The article emphasizes the challenges of adapting generative AI to text summarization and mentions past incidents where Google's generative AI technology produced significant errors, prompting questions about its readiness for such tasks. The underlying technical basis of Google's new experimental tool called:" SGE while browsing" is to:

Try and discern patterns in a training dataset to be able to create new data similar to that on which it has been trained. It means that generative AI is guessing the probable next likely word in a sentence at any time, rather than innately checking it against a ground truth…."Summarization models trained using supervised learning have existed for years and achieve high performance on a variety of tasks, using generative AI instead is fraught with all sorts of risks that aren’t worth it."

Beautiful Visualization: Looking at Data through the Eyes of Experts

Steele, J. and N. Iliinsky. Beautiful Visualization: Looking at Data through the Eyes of Experts. O'Reilly & Associates Inc., 2010. ISBN 1449379869.  

This book expresses how two dozen visualization experts demonstrate how to approach data visualization projects from a variety of disciplines – as artists, designers, commentators, scientists, analysts, statisticians etc. Visualization is defined as: 

the graphic presentation of data – portrayals meant to reveal complex information at a glance…Successful visualizations are beautiful not only for their aesthetic design, but also for the elegant layers of details that efficiently generate insight and new understanding".

The book begins by expressing "What is Beauty?"…

For a visual to truly be beautiful, it must go beyond merely being a conduit for information and offer some novelty: a fresh look at the data or a format that gives readers a spark of excitement and results in a new level of understanding. Well-understood formats (e.g., scatterplots) may be accessible and effective, but for the most part they no longer have the ability to surprise or delight us. Most often, designs that delight us do so not because they were designed to be novel, but because they were designed to be effective; their novelty is a byproduct of effectively revealing some new insight about the world. (P.19-20) … "Most importantly, beautiful visualizations reflect the qualities of the data that they represent, explicitly revealing properties and relationships inherent and implicit in the source data. As these properties and relationships become available to the reader, they bring new knowledge, insight, and enjoyment. " effectively their novelty is a byproduct of effectively revealing some new insight about the world." (P. 21)

The seven stages of the art of information visualization is discussed in depth (p. 33 " Most visualization stories begin with some kind of question that orients the viewer to the topic and context within which the data is most meaningful." (p. 34). The importance of interacting with data in ways that allow people to understand patterns is emphasized in discussions about visualizing social networks (P. 156-173).

Wordles are discussed in depth, starting with the question, "Is Wordle Good for Information Visualization?...(p. 72-78) and ending with how Wordles are actually used.

A Neural Attention Model for Abstractive Sentence Summarization

Rush, Alexander M., and Sumit Chopra, Jason Weston. "A Neural Attention Model for Abstractive Sentence Summarization". arXiv. 1509.00685v2. Accessed 1 October 2023, https://arxiv.org/abs/1509.00685v2.

This article discusses the challenge of text summarization in natural language understanding. It focuses on sentence-level summarization and the difference between extractive and abstractive summarization methods. The authors propose an attention-based summarization model called ABS (Attention-Based Summarization) and discuss its architecture, including the use of a neural language model and an attention-based encoder. They also experiment with extractive tuning to improve the system's performance. The article evaluates the ABS model's effectiveness against various baselines on headline generation tasks using different datasets. 

The paper goes on to explain what the ABS (Attention-Based Summarization) system is. – It is a neural summarization model designed to generate abstract summaries of text. It incorporates an attention-based encoder that aligns the input text with the generated summary. The model uses a neural language model to estimate the contextual probability of the next word in the summary. "The ABS system is trained on sentence summarization tasks and employs a beam-search decoder for generation."

The system also includes an extractive tuning feature to balance abstractive and extractive elements. How the ABS system compares to other summarization approaches, is discussed. The key features of the ABS's model architecture are a neural language model that estimates the contextual probability of the next word in the summary. It uses various encoders to process input text, including a Bag-of-Words encoder, a Convolutional encoder, and an Attention-Based encoder. The article uses two datasets for evaluation to assess the performance of the ABS system and to compare its baseline results. " "ABS outperforms TOPIARY and MOSES+ on most evaluation metrics, particularly ROUGE scores, which are commonly used for summarization evaluation."

ROUGE-C: A fully automated evaluation method for multi-document summarization

He, T. et al. "ROUGE-C: A fully automated evaluation method for multi-document summarization," IEEE International Conference on Granular Computing, Hangzhou, China, 2008, pp. 269-274, doi: 10.1109/GRC.2008.4664680 

ROUGE C is a fully automated evaluation method for multi-document summarization. It is based on the ROUGE metric, which is a widely used evaluation tool for summarization. ROUGE-C applies the ROUGE method alternatively by replacing the reference summaries with source document as well as query-focused information (if any), and therefore it enables a fully manual-independent way of evaluating multi-document summarization. Experiments conducted on the 2001 to 2005 DUC data showed that, with restraint of appropriate condition and some acceptable decreased efficiency, ROUGE-C correlated well with methods that depend on reference summaries, including human judgments.

This paper presents how to use ROUGE to evaluate summaries without human reference summaries. However, manual reference summaries written beforehand by assessors are indispensable for a ROUGE test. ROUGE-C works by first identifying the most important sentences in the source documents and query, if any. This is done using a variety of factors, such as the sentence's location in the document, its length, and its similarity to other sentences in the document. Once the most important sentences have been identified, ROUGE-C generates a summary by selecting a subset of these sentences. To evaluate the summary, ROUGE-C calculates the ROUGE score between the summary and the source documents and query, if any. The ROUGE score is a measure of the overlap between two sets of text, and it is calculated by counting the number of n-grams (sequences of n words) that appear in both sets of text.

ROUGE-C has been shown to correlate well with human judgments of summarization quality, and it is now widely used to evaluate multi-document summarization systems.

Evaluation of Text Summaries Based on Linear Optimization of Content Metrics

Rojas-Simon, Jonathan, Yulia Ledeneva and Rene Arnulfo Garcia-Hernandes. Evaluation of Text Summaries Based on Linear Optimization of Content Metrics. Book Series: Studies in Computational Intelligence, SCI, vol 1048. Springer, Cham. https://doi.org/10.1007/978-3-031-07214-7_9 

This book explains the challenges of Evaluation of Text Summaries (ETS). It sets out to demonstrate the hypothesis that linear optimization content metrics might improve the correlation between automatic and manual evaluation. They describe and provide experimental evidence that linear optimization improves the ETS.  They applied 'state-of-the-art evaluation methods that do not require human references. They explain how they developed a new methodology based on genetic algorithms that can optimize content metric linearly – based on how linear optimization has improve other natural language processing tasks. They also present a description of new considerations and perspectives that will address the ETS in the future.

A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA

Mojrian, Mohammad and Seyed Abolghasem Mirroshandel. "A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA". Expert Systems and Applications, vol. 171, June 2021, p. 114555. https://www.sciencedirect.com/science/article/abs/pii/S0957417420311994

This paper presents MTSQIGA, a novel multi-document text summarization approach that employs a modified quantum-inspired genetic algorithm (QIGA) to optimize extractive summarization. 

"In this paper, an extractive multi-document text summarization system, called MTSQIGA, is proposed which aims at satisfying the main purpose of summarization approaches, that is, providing the maximum coverage of presented content in source text and minimizing redundancy in the summary." (p.3)

The system focuses on maximizing coverage, relevance, and minimizing redundancy in the summary, utilizing a unique objective function comprising six sentence scoring measures. To ensure summary length compliance, the presented QIGA incorporates a modified quantum measurement and a self-adaptive quantum rotation gate. The evaluation was done using standardized collections of text summarization documents for testing of the performance of   their text summarization algorithms. (These benchmark datasets are known as DUC (Document Understanding Conference). The paper reports that the results demonstrated MTSQIGA's superior performance over existing state-of-the-art multi-document summarization methods. This study represents a pioneering application of quantum-inspired evolutionary algorithms (the use of quantum-inspired techniques to optimize and improve the process of generating text summaries) in the automatic text summarization field.

Natural language processing: state of the art, current trends and challenges

Khurana, D., Koli, A., Khatter, K. et al. "Natural language processing: state of the art, current trends and challenges". Multimed Tools Appl, vol. 82, 3713–3744, 2023. https://doi.org/10.1007/s11042-022-13428-4.

This paper provides insights into the various important terminologies of Language Processing and Natural Language Generation. NLP classifies NLP into two parts: " Natural Language Understanding or Linguistics, came into existence because humans wanted computers to understand the statements/word written in human languages. Natural Language Generation (is the task to understand and generate the text). Linguistics is the science of language which includes Phonology that refers to sound, Morphology word formation, Syntax sentence structure, Semantics syntax and Pragmatics which refers to understanding. The paper fulfills the objective to "discuss Natural Language Understanding (Linguistic) (NLU) and the Natural Language Generation (NLG)" pp 3715.NOTE – there is interesting detail regarding Machine translation and the latest changes in Google translation (see P. Applications of NLP pp. 3724.)

The Paper discusses:

1. Different levels of NLP and components of Natural Language Generation:
2. The history and evolution of NLP
3. The state o the art presenting various applications of NLP – the current trends and challenges.
4. Discussion on some available datasets, models, and evaluation metrics in NLP.

Automatic Text Summarization with Machine Learning — An overview

Gonçalves, Luís. "Automatic Text Summarization with Machine Learning — An overview". MEDIUM. 2020. 

This article provides a comprehensive overview of automatic text summarization, emphasizing its increasing importance due to the need for efficiently condensing large volumes of text data. The article discusses the various applications of text summarization in the field of natural language processing (NLP) and outlines two primary approaches: extraction-based and abstractive summarization. 

It addresses the challenges of automatic text summarization and highlights the machine learning models and techniques used in this area, such as sequence-to-sequence models, reinforcement learning, and adversarial processes. The article clearly explains the key concepts and developments in automatic text summarization with a focus on machine learning techniques.

ConcentriCloud: Word Cloud Visualization for Multiple Text Documents

Lohmann, Sylvia, et al. "ConcentriCloud: Word Cloud Visualization for Multiple Text Documents". IEEE, 2015. http://www.profitippliga.de/papers/77.pdf

This paper introduces ConcentriCloud. It is described as "a layered word cloud layout that merges

the words from several text documents into a single visualization. The weighted words are arranged in a concentric layout, with those representing the individual documents on the outer circle and the merged ones on inner circles. Interaction techniques allow us to analyze the word cloud composition and to provide details on demand." (P1) …This composition principle is "emphasized by the saturation of the background color, which increases with the level of aggregation." (p.1)

The paper discusses the research concerned with improving word clouds, since often visualizations do not distinguish different text documents, but the words are "treated as if they would all come from the same text source, even if they do not." (P.2).  The RAdCloud approach is discussed as it "most closely relates to the idea of ConcentriCloud." (p.2). See the schematic illustration of the composition of ConcentriCloud on p. 3 Fig 2 – through to P. 5. Furthermore, this implementation includes options to customize and interact with the visualization." (P.5). Figure 4 also shows two further modes of interaction: (1) highlighting of the word clouds that represent the corresponding documents when the user hovers a term in a cloud (in this case “phrase”), and (2) tooltips that appear for each word showing its overall number of occurrences in the document set and its distribution across the individual documents. Figure 5 shows an alternative layout for the word clouds. (p.5). Experts were used to answer questions about the dataset (which were visualizations of the Harry Potter novels.) (P.6). The pluses and minuses of the findings are expressed (P.6). the conclusion was that even with discovered limitations, the general approach has much potential… (P.7).

Word Storms: Multiples of Word Clouds for visual Comparison of Documents

Castella, Q and C. Sutton. "Word Storms: Multiples of Word Clouds for visual Comparison of Documents". Springer, 2005.  https://homepages.inf.ed.ac.uk/csutton/publications/castella14word.pdf

This paper introduces the concept of word storms. These are a group of word clouds designed for the visualization of a corpus of documents – not just for a single document. The author presents:

a series of principles for effective storms, arguing that the clouds in a storm should be built in a coordinated fashion, so that similar documents have similar clouds. We presented a novel algorithm that builds coordinated word storms, placing shared words in a similar location across clouds. Using both an automatic evaluation and a user study, we showed that coordinated word storms were markedly superior to independent word clouds for comparing and contrasting documents. (p.11)

Word storms are presented through using a novel algorithm that creates a "coordinated word storm, 

..in which words that appear in multiple documents are placed in the same location, using the same color and orientation, across clouds. This ensures that similar documents are represented by similar-looking word clouds, making them easier to compare and contrast visually. (p.1)

 A word cloud (such as Wordle which is one of the most popular word cloud generators) is defined as: "a graphical presentation of a document, usually generated by plotting the document's most common words in 2-dimensional space, with the word's frequency indicated by its font size." (p.1). The author explains that different types of storms can be constructed for different data analysis tasks.  Storm clouds can be built to compare individual documents, or documents written over a long period of time, or Hierarchies of documents – arranged in categories.

Word storms allow the user to analyze the corpus at a variety of levels. The principles behind effective word storms are discussed in detail on Page 2. Coordinated attribute selection – where the word that appears in more than one cloud, is displayed with the same color and orientation across clouds – The use of color improves the ways of encoding the relevance of a term in a document.  The use of an iterative algorithm helps to coordinate the layout of storm clouds. This is worked by "repeatedly using the 'spiral algorithm' – Thus enabling the iterative layout algorithm to change the location of the shared words to make them converge to the same position in all clouds." (p5) a Gradient approach is also used to solve an optimization problem thus improving the initial layout significantly. The evaluation of the study is divided into 3 parts – qualitative analysis, an automatic analysis, and a user study. This is followed by a Qualitative Analysis section that discusses word storms in a qualitative fashion. 

Word clouds were inspired by tag clouds – but this approach, in common with Wordle, does not attempt to resolve cases of synonymy (similar meanings) or polysemy (related meanings).

A Quick Introduction to Text Summarization in Machine Learning

Education Ecosystem. "A Quick Introduction to Text Summarization in Machine Learning." Towards Data Science. Accessed 9 September 2023, https://towardsdatascience.com/a-quick-introduction-to-text-summarization-in-machine-learning-3d27ccf18a9f.

This article provides an overview of text summarization in the context of machine learning and natural language processing (NLP). It discusses the importance of automatic text summarization in handling large volumes of data. It introduces two main approaches (extraction-based and abstraction-based summarization) and outlines the basic steps of how a text summarization algorithm works. It focuses mostly on the extraction-based approach as a supervised machine-learning problem. This technique of text summarization "has proved to be critical in quickly and accurately summarizing voluminous texts."  With big data's ever-expansion, this task would be prohibitively "expensive and time-consuming if done without machines".

Parallel sets: visual analysis of categorical data

Bendix, F., R. Kosara and H. Hauser, "Parallel sets: visual analysis of categorical data," IEEE Symposium on Information Visualization INFOVIS 2005., Minneapolis, MN, USA, 2005, pp. 133-140, doi: 10.1109/INFVIS.2005.1532139.

The discrete nature of categorical data makes it a particular challenge for visualization. Methods that work very well for continuous data are often hardly usable with categorical dimensions. Only a few methods deal properly with such data, mostly because of the discrete nature of categorical data, which does not translate well into the continuous domains of space and color. Parallel sets are a new visualization method that adopts the layout of parallel coordinates but substitutes the individual data points by a frequency-based representation. This abstracted view, combined with a set of carefully designed interactions, supports visual data analysis of large and complex data sets. The technique allows efficient work with meta data, which is particularly important when dealing with categorical datasets. By creating new dimensions from existing ones, for example, the user can filter the data according to his or her current needs. The paper also presents the results from an interactive analysis of CRM data using parallel sets. It demonstrates how the flexible layout eases the process of knowledge crystallization, especially when combined with a sophisticated interaction scheme.

Enhancing unsupervised neural networks-based text summarization with word embedding and ensemble learning

Alami, Navil. "Enhancing unsupervised neural networks-based text summarization with word embedding and ensemble learning". Expert Systems with Applications, Vol 123, 2019, pp. 195-211. Accessed 9 September 2023, https://doi.org/10.1016/j.eswa.2019.01.037.

As noted in the Abstract of this paper, this paper seeks to "enhance the quality of ATS by integrating unsupervised deep neural network techniques with word embedding approach"(P. 1). In defining the two distinct categories of text summarization: extractive and abstractive, the paper explains them this way:

Extractive summarization, known as sentence ranking, consists of ranking and extracting sentences according to the most important information circulated in the text. However, in the abstractive category, the summary is built in re-writing new sentences containing the most important idea of the original text. The abstractive summarization needs large amounts of linguistic resources and human generated ontologies. Due to the lack of natural language resources, abstractive approaches are very difficult, while the extractive approaches are most widespread. (P.2)

Finally, this paper introduces "unsupervised learning algorithms based on neural networks for automatic text summarization". These algorithms are performed based on the vector representation of words."

In recent years, the increased strength of deep learning methods especially for leaning unsupervised tasks makes this representation more powerful and more relevant than the classical BOW representation through word2vec. On the other hand, ensemble learnings technique usually produces more…(P.6)