Semantic-Summarizer: Semantics-based text summarizer for English language text

Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such a gigantic amount of textual content, human text summarization becomes impractical since it is expensive and consumes a lot of time and effort. So, developing automatic text summarization (ATS) systems is becoming increasingly essential. ATS approaches are either extractive or abstractive. The extractive approach is simpler and faster than the abstractive approach. This work proposes an extractive ATS system that aims to extract a small subset of sentences from a large multi-document text. First, the whole text is preprocessed by applying some natural language processing techniques such as sentences segmentation, words tokenization, removal of stop-words, and stemming to provide a structured representation of the original document collection. Based on this structured representation, the ATS problem is formulated as a multi-objective optimization (MOO) problem that optimizes the extracted summary to maintain the coverage of the main text content while avoiding redundant information. Secondly, an evolutionary sparse multi-objective algorithm is developed to solve the formulated large-scale MOO. The output of this algorithm is a set of non-dominated summaries (Pareto front). A novel criterion is proposed to select the target summary from the Pareto front. The proposed ATS system has been examined using (DUC) datasets, and the output summaries have been evaluated using (ROUGE) metrics and compared with the literature.

One of the most important tasks in NLP (Natural Language Processing) is automatic text summarization which aims to distill vast amounts of textual information into a brief summary while maintaining its essential meaning. Notwithstanding notable progress earlier research in this field has been constrained by the incapacity to accurately summarize various datasets and capture contextual subtleties. Scalability generalization and striking a balance between summary coherence and informativeness are common issues with current models. Using the Conditional Generative Adversarial Network (CGAN) technique we present a novel framework for automatic text summarization that incorporates data augmentation in order to overcome these constraints. In order to ensure that the text is clean and prepared for additional analysis the suggested framework starts with a thorough pre-processing phase that includes tokenization hashtag and username elimination stop word removal punctuation removal stemming and lemmatization. The Robustly Optimised BERT Approach (RoBERTa) which we use for word embedding greatly improves contextual representation and text comprehension. A hybrid approach that combines ShuffleNet V2 and SE-ResNeXt-101 is used for feature extraction ensuring effective extraction of discriminative features. Ultimately the Modified Electric Eel Foraging Optimisation (MEEFO) algorithm is used to optimise the summary generation. This algorithm explores the trade-off between precision and recall in order to fine-tune the summary quality. Experimental results show that the suggested method is more effective than current approaches with notable gains in ROUGE-L scores and overall summary quality across a number of benchmark datasets. During training and testing the model achieves higher accuracy and lower loss demonstrating its generalizability and resilience.

Automatic text summarization is a popular area in Natural Language Processing and Machine Learning. In this work, we adopt a graph-based text summarization approach, using PageRank algorithm, for automatically summarizing Konkani text documents. Konkani is an Indo-Aryan language spoken primarily in the state of Goa, which is on the west coast of India. It is a low-resource language with limited language processing tools. Such tools are readily available in other popular languages of choice for automatic text summarization, like English. The Konkani language dataset used for this purpose is based on Konkani folktales. We examine the impact of various language-independent and language-dependent similarity measures on the construction of the graph. The language-dependent similarity measures use pre-trained fastText word embeddings. A fully connected undirected graph is constructed for each document with the sentences represented as the graph's vertices. The vertices are connected to each other based on how strongly they are related to one another. Thereafter, PageRank algorithm is used for ranking the scores of the vertices. The top-ranking sentences are used to generate the summary. ROUGE toolkit was used for evaluating the quality of these system-generated summaries, and the performance was evaluated against human generated “gold-standard” abstracts and also compared with baselines and benchmark systems. The experimental results show that language-independent similarity measures performed well compared to language-dependent similarity measures despite not using language-specific tools, such as stop-words list, stemming, and word embeddings.

Automatic text summarization involves extracting relevant details from the contents of input text documents for generating summaries. This area of Natural Language Processing is widely researched, especially with popular languages like English. There is a need to extend this work to less commonly spoken languages of the world. This paper presents a language-independent text summarization approach using Latent Semantic Analysis in Konkani language. Konkani is a low-resource language with limited language processing tools, stop-word list, etc. Latent Semantic Analysis (LSA) is an unsupervised algebraic method that finds latent semantic structures to be used for performing extractive text summarization. We examined well-known Latent Semantic Analysis-based sentence selection approaches on our dataset, constructed using books on Konkani folk tales written in Devanagari script. The results of the experiments indicated that LSA-based approaches can produce promising summaries, with the Cross method performing the best in most metrics.KeywordsAutomatic text summarizationLatent semantic analysisKonkaniLow-resourceSingular value decompositionExtractive text summarization

Deep learning is currently developing very fast in the NLP field and has achieved many amazing results in the past few years. Automatic text summarisation means that the abstract of the document is automatically summarised by a computer program without changing the original intention of the document. There are many application scenarios for automatic summarisation, such as news headline generation, scientific document abstract generation, search result segment generation, and product review summarisation. In the era of internet big data in the information explosion, if the short text can be employed to express the main connotation of information, it will undoubtedly help to alleviate the problem of information overload. In this paper, a model based on the long short-term memory network is presented to automatically analyse and summarise Chinese articles by using the seq2seq+attention models. Finally, the experimental results are attached and evaluated.

Information overload is a real challenge that product designers face whiles trying to glean insight from online product reviews. Opinion summaries are limited in the richness of insight and text summarizations pose the risk of information loss for aspect based analysis. Although much effort has been spent yearly to advance the research in search, opinion analysis and text summarization, the same cannot be said for the provision of practical models and tools to leverage these advancements. In this work, we proposed a graph-based method centred on the Labelled Property Graph, sentiment analysis and text summarization. It indexes all tokens and opinions and allows for an explorative approach to aspect sentiment analysis whiles providing targeted sentence extracts for text summarizations through opinion-aware search. We show the limitations of text summarization for designers and show how our model can avoid them with expressive pattern matching and property filtering without reprocessing aspect sentiments.

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 technique for reducing lengthy passages of text into smaller portions. The goal is to develop a logical and fluent summary that only includes the document's major ideas. It's a important task in Natural Language Processing (NLP) that undoubtedly has a significant impact on synthesization of lengthy documents. With the rise of the digital documentation and publication, devoting time to thoughtfully read an article, document, or book in order to determine its relevance is no longer an option, especially given time constraints. In machine learning and NLP, automatic text summarization is a generic problem. A comparison of text summarization applying the Term Frequency ~ Inverse Document Frequency, Latent Semantic Analysis and Text Rank algorithms for the Malayalam language is presented in this research paper.

In India's diverse educational landscape, linguistic barriers and hearing impairments present significant challenges to effective learning. The research introduces an innovative NLP-driven solution aimed at overcoming these hurdles by offering a comprehensive educational platform. This platform includes features such as subtitle generation, text summarization, Q&A generation for content recall, multilingual translation, and Indian Sign Language (ISL) support. Leveraging a fine-tuned Whisper model for Speech-to-Text Translation (STT) and conducting a comparative study of BERT, T5, and TextRank for subtitle summarization, this approach ensures optimized language understanding and content generation across various NLP applications. Through the utilization of ConceptNet and BERT for question answering tasks, the system demonstrates impressive performance metrics, including a 95.12% accuracy rate in subtitle generation and an average WER of 3.43%. While manual evaluation was necessary due to the absence of specific datasets, this innovative approach represents an advancement in addressing educational accessibility gaps in India, fostering the Right to Education to all individual users in an educational environment.

As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.

The evolution of Artificial Intelligence (AI) gave insight into how humans should interact with machines. Natural Language Processing (NLP) is an area under AI, which aims to minimize the communication gap between human and machine. Text summarization is a sub-domain of NLP, which primarily focuses on summarizing text documents into a meaningful summary. Text summarization is useful in the areas, such as media monitoring, search engine optimization (SEO), question answering systems, etc. Lots of research works are carried out in the area of text summarization. A majority of the research works are adopted syntactic approaches. However, the semantic based approaches provide more meaningful summary compared to syntactic approaches. Due to the complexity of the semantic based methods, only few works are reported especially in Indian languages. In this paper, an overview of various semantic based text summarization methods are explored. An extensive review is done in languages, such as English, Arabic, Chinese, Hindi, Kannada, Tamil, and Malayalam. At the end of this survey, a critical analysis was done on various semantic based text summarization techniques. This analysis will not only give insights to potential prospective researchers, but also come up with an improved framework or method for semantic based abstractive text summarization.

With the fast development of large software projects, automatic code summarization techniques, which summarize the main functionalities of a piece of code using natural languages as comments, play essential roles in helping developers understand and maintain large software projects. Many research efforts have been devoted to building automatic code summarization approaches. Typical code summarization approaches are based on deep learning models. They transform the task into a sequence-to-sequence task, which inputs source code and outputs summarizations in natural languages. All code summarization models impose different input size limits, such as 50 to 10,000, for the input source code. However, how the input size limit affects the performance of code summarization models still remains under-explored. In this article, we first conduct an empirical study to investigate the impacts of different input size limits on the quality of generated code comments. To our surprise, experiments on multiple models and datasets reveal that setting a low input size limit, such as 20, does not necessarily reduce the quality of generated comments. Based on this finding, we further propose to use function signatures instead of full source code to summarize the main functionalities first and then input the function signatures into code summarization models. Experiments and statistical results show that inputs with signatures are, on average, more than 2 percentage points better than inputs without signatures and thus demonstrate the effectiveness of involving function signatures in code summarization. We also invite programmers to do a questionnaire to evaluate the quality of code summaries generated by two inputs with different truncation levels. The results show that function signatures generate, on average, 9.2% more high-quality comments than full code.

Instructional videos have become a popular tool for teaching complex topics in computer networking. However, these videos can often be lengthy and time-consuming, making it difficult for learners to obtain the key information they need. In this study, we propose an approach that leverages automatic summarization and language models to generate concise and informative summaries of instructional videos. To enhance the performance of the summarization algorithm, we also incorporate video attributes that provide contextual information about the video content. Using a dataset of computer networking tutorials, we evaluate the effectiveness of the proposed method and show that it significantly improves the quality of the video summaries generated. Our study highlights the potential of using language models in automatic summarization and suggests that incorporating video attributes can further enhance the performance of these models. These findings have important implications for the development of effective instructional videos in computer networking and can be extended to other domains as well.

Automatic text summarization is a subarea of natural language processing that generates a summary of the text by keeping its key points. The research work done on summarizing low-resourced language text is very limited. In India, the Hindi language is being spoken by central and north Indian people and only a few research works have been done on abstractive summarization of Hindi language. Having matras in Hindi makes it difficult to tokenize so it is difficult to summarize Hindi text using abstractive text summarization. In the proposed method, abstractive Hindi text summarization is done using transliteration and fine-tuning. In this work, the model is trained to generate both summaries and headlines. ROUGE-score and BERT-score have been utilized to check summary quality. A new semantic similarity score-based performance measure is also proposed to measure semantic similarity between reference summaries and predicted summaries. Using the proposed method, we have achieved the highest 55.16 ROUGE score, 0.80 BERT score, and 0.98 similarity score. Along with these performance measures, human evaluation of predicted summaries is also done and it is found that summaries and headlines were generated at a human-acceptable level.

There have been considerable advancements in Text Summarization over the last few years. There are two ways to text summarization: one is based on natural language processing (NLP), and the other is based on deep learning. In the realm of NLP, text summarization is the most intriguing and challenging task. NLP stands for Natural Language Processing, which studies and manipulates human language by computers. Because of the massive increase in information and data, it has become critical. Text summarization is creating a thorough and meaningful summary of text from various sources such as books, news stories, research papers, and tweets, among others. Large text documents that are difficult to summarize manually are the research subject. The job of condensing a piece of text into a shorter version, minimizing the size of the original text while maintaining key informative components and content meaning, is known as summarising. Because human text summarising is a time-consuming and intrinsically tiresome process, automating it is gaining popularity and hence acts as a potent stimulus for academic study.