Manual Summarization Research Articles

Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study.

Comprehensive session summaries enable effective continuity in mental health counseling, facilitating informed therapy planning. However, manual summarization presents a significant challenge, diverting experts' attention from the core counseling process. Leveraging advances in automatic summarization to streamline the summarization process addresses this issue because this enables mental health professionals to access concise summaries of lengthy therapy sessions, thereby increasing their efficiency. However, existing approaches often overlook the nuanced intricacies inherent in counseling interactions. This study evaluates the effectiveness of state-of-the-art large language models (LLMs) in selectively summarizing various components of therapy sessions through aspect-based summarization, aiming to benchmark their performance. We first created Mental Health Counseling-Component-Guided Dialogue Summaries, a benchmarking data set that consists of 191 counseling sessions with summaries focused on 3 distinct counseling components (also known as counseling aspects). Next, we assessed the capabilities of 11 state-of-the-art LLMs in addressing the task of counseling-component-guided summarization. The generated summaries were evaluated quantitatively using standard summarization metrics and verified qualitatively by mental health professionals. Our findings demonstrated the superior performance of task-specific LLMs such as MentalLlama, Mistral, and MentalBART evaluated using standard quantitative metrics such as Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, ROUGE-L, and Bidirectional Encoder Representations from Transformers Score across all aspects of the counseling components. Furthermore, expert evaluation revealed that Mistral superseded both MentalLlama and MentalBART across 6 parameters: affective attitude, burden, ethicality, coherence, opportunity costs, and perceived effectiveness. However, these models exhibit a common weakness in terms of room for improvement in the opportunity costs and perceived effectiveness metrics. While LLMs fine-tuned specifically on mental health domain data display better performance based on automatic evaluation scores, expert assessments indicate that these models are not yet reliable for clinical application. Further refinement and validation are necessary before their implementation in practice.

Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation.

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both expensive and time-consuming, making it impractical for humans to handle vast amounts of data. Consequently, the need for ATS systems has become evident. These systems encounter challenges such as ensuring comprehensive content coverage, determining the appropriate length of the summary, addressing redundancy, and maintaining coherence in the generated summary. Researchers are actively addressing these challenges by employing Natural Language Processing (NLP) techniques. While traditional methods exist for generating summaries, they often fall short of addressing multiple aspects simultaneously. To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. The performance of this enhanced algorithm is assessed by comparing it with state-of-the-art algorithms using the DUC2002 dataset. Experimental results demonstrate that the proposed algorithm significantly outperforms the compared approaches.

Dilated convolution for enhanced extractive summarization: A GAN-based approach with BERT word embedding

Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance.

Automated Text Summarization as A Service

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.

Effective Approach for Fine-Tuning Pre-Trained Models for the Extraction of Texts From Source Codes

This study introduces SR-Text, a robust approach leveraging pre-trained models like BERT and T5 for enhanced text extraction from source codes. Addressing the limitations of traditional manual summarization, our methodology focuses on fine-tuning these models to better understand and generate contextual summaries, thus overcoming challenges such as long-term dependency and dataset quality issues. We conduct a detailed analysis of programming language syntax and semantics to develop syntax-aware text retrieval techniques, significantly boosting the accuracy and relevance of the texts extracted. The paper also explores a hybrid approach that integrates statistical machine learning with rule-based methods, enhancing the robustness and adaptability of our text extraction processes across diverse coding styles and languages. Empirical results from a meticulously curated dataset demonstrate marked improvements in performance metrics: precision increased by 15%, recall by 20%, and an F1 score enhancement of 18%. These improvements underscore the effectiveness of using advanced machine learning models in software engineering tasks. This research not only paves the way for future work in multilingual code summarization but also discusses broader implications for automated software analysis tools, proposing directions for future research to further refine and expand this methodology.

Leveraging GPT-4 for food effect summarization to enhance product-specific guidance development via iterative prompting

Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment, which provides the basis of recommendations for fasting and fed bioequivalence studies to guide the pharmaceutical industry for developing generic drug products. However, manual summarization of food effect from extensive drug application review documents is time-consuming. Therefore, there is a need to develop automated methods to generate food effect summary. Recent advances in natural language processing (NLP), particularly large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability with regard to the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach,iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the iterative prompting process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. Taken together, these results strongly suggest a great potential for GPT-4 to draft food effect summaries that could be reviewed by FDA professionals, thereby improving the efficiency of the PSG assessment cycle and promoting generic drug product development.

A Transformer based approach for Abstractive Text Summarization of Radiology Reports

Abstractive text summarization has emerged as a promising approach for generating conciseand informative summaries from radiology reports. The topic of research focuses on developing a mechanism for abstractive text summarization specifically tailored for radiology reports, to generate informative summaries from the voluminous and complex information contained within reports. Manual summarization is time-consuming and prone to errors, while automated techniques can save time, reduce human bias, and improve the overall quality of the generated summaries. The mechanism involves preprocessing the text with NLP techniques, utilizing deep learning-based architectures and transformer models, and generating summaries that capture the essence of the original reports in a more concise form.Challenges such as handling complex medical terminologies, incorporating contextual information, and evaluating the quality of generated summaries are important considerations in this mechanism. The potential applications of radiology report summarization include improving report readability, facilitatingdecision-making, and enabling large-scale data analytics. In this research, Biobart-V2 model is used for summarization and Rouge-L value of 69.42% is being achieved whereas the dataset used is MIMIC III.Further research is needed to address the remaining challenges in this domain and integrate summarization into clinical practice for more effective and efficient radiology report interpretation and patient care.

Automated Notes Maker from Audio Recordings

Automated note-taking software uses machine learning and natural language processing to transcribe spoken words from audio recordings. In the current fast-paced environment, individuals and organisations regularly have to manage enormous amounts of data in the form of audio recordings, video recordings, and text documents. The manual summarization and transcription of such data can be laborious and time-consuming. This problem has been resolved by automated note-taking software for audio recordings. Natural language processing (NLP) and machine learning techniques are used by these technologies to transform spoken words into text, which is subsequently used to build a summary or set of notes. This project report looks at the effectiveness and utilisation of automated note-taking software for audio recordings.To transform the audio recordings of online classes into text-based PDF or Word documents, which are voice-based in correctness, a module for automated notes creator from audio recordings is shown in this presentation.

EDCOSUM: Text extractive summarization framework based on edge information with coreference resolution

Automatic Text Summarization(ATS) is distinctly beneficial due to a vast amount of textual data and time-consuming manual summarization. In order to enhance ATS for single document in huge datasets, a new extractive graph framework - text extractive SUMmarization framework based on EDge information with COreference resolution EDCOSUM is proposed in this paper that relies on coreference resolution, adding edge information in word-level graph and a sentence-ranking strategy. EDCOSUM combines the graph-based and statistical-based extractive summarization methods. It is a general method for any document (not limited to a specific domain). Moreover, two ranking strategies(sentence and LSA ranking strategy) are proposed for sentence selection. A set of extensive experiments on CNN/Daily Mail and NEWSROOM are conducted for investigating the proposed method. The widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation(ROUGE) is utilized to evaluate EDCOSUM. Compared to the state-of-the-art ATS methods, EDCOSUM achieves a competitive result by improvements of over the highest scores in the literature for metrics ROUGE-1, ROUGE-2 and ROUGE-L respectively.

Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain.

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.

Efficient GAN-based Method for Extractive Summarization

Background and Objectives: Text summarization plays an essential role in reducing time and cost in many domains such as medicine, engineering, etc. On the other hand, manual summarization requires much time. So, we need an automated system for summarizing. How to select sentences is critical in summarizing. Summarization techniques that have been introduced in recent years are usually greedy in the choice of sentences, which leads to a decrease in the quality of the summary. In this paper, a non-greedily method for selecting essential sentences from a text is presented.Methods: The present paper presents a method based on a generative adversarial network and attention mechanism called GAN-AM for extractive summarization. Generative adversarial networks have two generator and discriminator networks whose parameters are independent of each other. First, the features of the sentences are extracted by two traditional and embedded methods. We extract 12 traditional features. Some of these features are extracted from sentence words and others from the sentence. In addition, we use the well-known Skip-Gram model for embedding. Then, the features are entered into the generator as a condition, and the generator calculates the probability of each sentence in summary. A discriminator is used to check the generated summary of the generator and to strengthen its performance. We introduce a new loss function for discriminator training that includes generator output, real and fake summaries of each document. During training and testing, each document enters the generator with different noises. It allows the generator to see many combinations of sentences that are suitable for quality summaries.Results: We evaluate our results on CNN/Daily Mail and Medical datasets. Summaries produced by the generator show that our model performs better than other methods compared based on the ROUGE metric. We apply different sizes of noise to the generator to check the effect of noise on our model. The results indicate that the noise-free model has poor results.Conclusion: Unlike recent works, in our method, the generator selects sentences non-greedily. Experimental results show that the generator with noise can produce summaries that are related to the main subject.

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

Development of a two-step LDA based aspect extraction technique for review summarization

ABSTRACT Summarization of online reviews by customers is a popular practice for evaluation of products or services. As the reviews accumulate, the large size and the unstructured nature of the reviews hinder manual summarization. Automatic categorization of the reviews as a whole into only positive and negative group cannot represent a clear picture. An aspect based automatic summarization technique can provide better visualization. However, automatic extraction of proper aspects from the huge reviews of any product is not very easy. There are some research works in this direction, but any definite method is yet to come. In this work, a two-step Latent Dirichlet Allocation (LDA) technique, which is popularly used for topic modelling has been developed for efficient aspect extraction. The method has been evaluated by simulation experiments on Amazon product reviews and Yelp restaurant and hotel reviews. The results have been found quite matching with human annotated results.

EdgeSumm: Graph-based framework for automatic text summarization

Searching the Internet for a certain topic can become a daunting task because users cannot read and comprehend all the resulting texts. Automatic Text summarization (ATS) in this case is clearly beneficial because manual summarization is expensive and time-consuming. To enhance ATS for single documents, this paper proposes a novel extractive graph-based framework “EdgeSumm” that relies on four proposed algorithms. The first algorithm constructs a new text graph model representation from the input document. The second and third algorithms search the constructed text graph for sentences to be included in the candidate summary. When the resulting candidate summary still exceeds a user-required limit, the fourth algorithm is used to select the most important sentences. EdgeSumm combines a set of extractive ATS methods (namely graph-based, statistical-based, semantic-based, and centrality-based methods) to benefit from their advantages and overcome their individual drawbacks. EdgeSumm is general for any document genre (not limited to a specific domain) and unsupervised so it does not require any training data. The standard datasets DUC2001 and DUC2002 are used to evaluate EdgeSumm using the widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation (ROUGE). EdgeSumm gets the highest ROUGE scores on DUC2001. For DUC2002, the evaluation results show that the proposed framework outperforms the state-of-the-art ATS systems by achieving improvements of 1.2% and 4.7% over the highest scores in the literature for the metrics of ROUGE-1 and ROUGE-L respectively. In addition, EdgeSumm achieves very competitive results for the metrics of ROUGE-2 and ROUGE-SU4.

Extractive and Abstractive Text Summarization Techniques

Text summarization generates an abstract version of information on a particular topic from various sources without modifying its originality. It is essential to dig information from the large repository of data, thereby eliminating the irrelevant information. The manual summarization consumes a large amount of time and hence an automated text summarization model is required. The summarization can be performed from a single source or multiple sources. The Natural Language Processing (NLP) based text summarization can be generally categorized as abstractive and extractive methods. The extractive methods mine the essential text from the document whereas the abstractive methods summarize the document by rewriting. The extractive summarization methods rely on topics and centrality of the document. The abstractive techniques transform the sentences based on the language resources available. This paper deals with the study of extractive as well as abstractive strategies in text summarization. Overall objective of this paper is to provide a significant direction to the researchers to learn about different strategies applied in text summarization.

Intelligent compilation of patent summaries using machine learning and natural language processing techniques

Patents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to understand how to re-create the new and useful invention but restricts the manufacturing unless the owner licenses or enters into a legal agreement to sell ownership of the patent. Patents reward the costly research and development efforts of inventors while spreading new knowledge and accelerating innovation. This research uses artificial intelligence natural language processing, deep learning techniques and machine learning algorithms to extract the essential knowledge of patent documents within a given domain as a means to evaluate their worth and technical advantage. Manual patent abstraction is a time consuming, labor intensive, and subjective process which becomes cost and outcome ineffective as the size of the patent knowledge domain increases. This research develops an intelligent patent summarization methodology using artificial intelligence machine learning approaches to allow patent domains of extremely large sizes to be effectively and objectively summarized, especially for cases where the cost and time requirements of manual summarization is infeasible. The system learns to automatically summarize patent documents with natural language texts for any given technical domain. The machine learning solution identifies technical key terminologies (words, phrases, and sentences) in the context of the semantic relationships among training patents and corresponding summaries as the core of the summarization system. To ensure the high performance of the proposed methodology, ROUGE metrics are used to evaluate precision, recall, accuracy, and consistency of knowledge generated by the summarization system. The Smart machinery technologies domain, under the sub-domains of control intelligence, sensor intelligence and intelligent decision-making provide the case studies for the patent summarization system training. The cases use 1708 training pairs of patents and summaries while testing uses 30 randomly selected patents. The case implementation and verification have shown the summary reports achieve 90% and 84% average precision and recall ratios respectively.

Cell‐type specific analysis of translating RNAs in developing flowers reveals new levels of control

Determining both the expression levels of mRNA and the regulation of its translation is important in understanding specialized cell functions. In this study, we describe both the expression profiles of cells within spatiotemporal domains of the Arabidopsis thaliana flower and the post-transcriptional regulation of these mRNAs, at nucleotide resolution. We express a tagged ribosomal protein under the promoters of three master regulators of flower development. By precipitating tagged polysomes, we isolated cell type-specific mRNAs that are probably translating, and quantified those mRNAs through deep sequencing. Cell type comparisons identified known cell-specific transcripts and uncovered many new ones, from which we inferred cell type-specific hormone responses, promoter motifs and coexpressed cognate binding factor candidates, and splicing isoforms. By comparing translating mRNAs with steady-state overall transcripts, we found evidence for widespread post-transcriptional regulation at both the intron splicing and translational stages. Sequence analyses identified structural features associated with each step. Finally, we identified a new class of noncoding RNAs associated with polysomes. Findings from our profiling lead to new hypotheses in the understanding of flower development.

A new approach to automatic speech summarization

This paper proposes a new automatic speech summarization method. In this method, a set of words maximizing a summarization score is extracted from automatically transcribed speech. This extraction is performed according to a target compression ratio using a dynamic programming (DP) technique. The extracted set of words is then connected to build a summarization sentence. The summarization score consists of a word significance measure, a confidence measure, linguistic likelihood, and a word concatenation probability. The word concatenation score is determined by a dependency structure in the original speech given by stochastic dependency context free grammar (SDCFG). Japanese broadcast news speech transcribed using a large-vocabulary continuous-speech recognition (LVCSR) system is summarized using our proposed method and compared with manual summarization by human subjects. The manual summarization results are combined to build a word network. This word network is used to calculate the word accuracy of each automatic summarization result using the most similar word string in the network. Experimental results show that the proposed method effectively extracts relatively important information by removing redundant and irrelevant information.