A Combined Approach of Text Summarization using different Keyword Extraction Techniques

Text summarization is one of the challenging and engrossing problems in the field of Natural Language Processing. The goal of Text Summarization is to read, understand and extract meaningful information from the unstructured data. The idea behind summarization is to get concise and precise summary from the original document. We used Sentence Ranking method based on Word Frequency to determine the important sentences in original text. The Summarizer uses three features to determine important Sentence in a document. The Term Frequency, similarity with the Title and Sentence score are the features considered. Values are assigned to each word and a sentence in a document and create a score for each word and a sentence. Normalize the scores to avoid raw scores that skew the results. The Scores of top 'N' sentences are taken for summarization. Text cleaning, Sentence tokenization, Word tokenization, Normalized Word frequency table generation, and Summarization are steps used in the Sentence Ranking Method. The method is implemented on WikiHow dataset. The results of proposed model are evaluated with ROUGE toolkit which has three evaluation metrics- Precision, Recall and F-Score.

In general, video-based learning contains rich media information, but displaying an entire video linearly is time-consuming. As an alternative, video summarization techniques extract important content provides short but informative fragments. In this paper, a video learning platform (KVSUM: Keyword-based Video Summarization) is presented that integrates image processing, text summarization, and keyword extraction techniques. Without human annotators, the learning platform can process an input video and transform it into online learning materials automatically. The video frames are first split from a given video while the transcription is used to generate the text summary and keywords. In the current study, the video surrogates are composed of extracted keywords, text and video summaries, and video frames. In other words, KVSUM is able to provide both visual and verbal surrogates. In order to validate the effect of surrogates in the KVSUM, a comparison with another video surrogate, the fast forward (FF), to evaluate learners' comprehension to video contents. Sixty undergraduate students took part in examining two different video surrogates. The experimental results show that KVSUM had a more positive effect than FF in comprehension to videos. In terms of system usage and satisfaction, KVSUM is significantly more attractive than FF to learners.

Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims to enhance the quality of extractive Indonesian automatic text summarization with considering the quality of structured representation of text. This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. Then, SPM is combined with feature-based approach using sentence scoring method to produce summary. The experiment result using IndoSum dataset shows that even though the combination of SPM and sentence scoring can increase the precision value of recall-oriented understudy for gisting evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L, from 0.68 to 0.76, 0.54 to 0.69, and 0.51 to 0.72. Especially, combination of SPM and Sentence Scoring can enhance precision, recall, and f-measure of ROUGE-L that consider the order of word occurance in measurement. SPM increases ROUGE-L f-measure value of sentence scoring from 0.32 to 0.36. Moreover, combination of sentence scoring and SPM is better than SumBasic that used as feature-based approach in the previous Indonesian text summarization research.

Increased availability of the Internet and social media has created another ‘world of data’ comprised of text, audio and video files. It is very difficult for a user to get the accurate summary or to comprehend the relevant and important items from the available media. Additionally, readers or evaluators of these data files are interested only in the relevant content or summary to be retrieved in the less duration from the source files. Automatic text summarization (ATS) is the only way to summarize single or multiple documents to obtain relevant content from the source files. Available ATS systems generate bad summaries and take a lot of time and space for long documents due to inaccurate encoding. Therefore, in this work, we have introduced an approach for extractive text summarization using sentence ranking. Experiments have been performed over BBC and CNN news datasets and evaluated in terms of ROUGE using N-gram Language Model. The quantitative values of the metrics show the effectiveness of the proposed approach for news datasets.

Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage limitations. The huge increase of data emerging by the progress of technology and the various sources makes automatic text summarization of large scale of data a challenging task. We propose an approach for automatic text summarization of large scale Arabic multiple documents using Genetic algorithm and MapReduce parallel programming model. The approach insures scalability, speed and accuracy in summary generation. It eliminates sentence redundancy and increases readability and cohesion factors between the sentences of summaries. The experiments resulted in acceptable precision and recall scores. This indicates that the system successfully identifies the most important sentences. In Addition to all to that, the approach provided up to 10x speedup score, which is faster than on a single machine. Therefore, it can deal with large-scale datasets successfully. Finally, the efficiency score of the proposed approach indicates that the large data set utilizes the available resources up to 62%.

Large language models such as GPT-3 have revolutionised how we conduct natural language processing tasks by applying abundant pre-trained linguistic information. Other extraction models exhibit a brief scope of attention, making it difficult to map different words and phrases together. We propose one that takes the ability of the LLMs to generate fitting and suitable keywords for research works. Individuals have traditionally solved the keyword extraction tasks using algorithms such as Text Rank, TF-IDF (Term Frequency Inverse Document Frequency), RAKE (Rapid Automatic Keyword Extraction), and so many others. From the Comparative Analysis of Ten Large Language Models, we identify the GPT-2 model to be most suited for the process of Keyword Extraction.

Text summarization is the process of creating a shorter version of one or more text documents. Automatic text summarization has become an important way of finding relevant information in large text libraries or in the Internet. Extractive text summarization techniques select entire sentences from documents according to some criteria to form a summary. Sentence scoring is the technique most used for extractive text summarization, today. Depending on the context, however, some techniques may yield better results than some others. This paper advocates the thesis that the quality of the summary obtained with combinations of sentence scoring methods depend on text subject. Such hypothesis is evaluated using three different contexts: news, blogs and articles. The results obtained show the validity of the hypothesis formulated and point at which techniques are more effective in each of those contexts studied.

Text summarization is an emerging technique for finding out the summary of the text document. Text summarization is nothing but summarizing the content of given text document. Text summarization has got so uses such as Due to the massive amount of information getting increased on internet; it is difficult for the user to go through all the information available on web. Summarization techniques need to be used to reduce the user's time in reading the whole information available on web. In this paper, we propose an automatic text summarization technique using both linguistic and statistical features using successive threshold for finding the summary i.e important sentences from the given input text document. Here the sentences are selected for summary based on the weight of the sentence. The weight of the sentences is calculated based on the statistical and linguistic features. Our approach assigns scores to the sentences by weighting the features like term frequency, word occurrences, and noun weight, phrases etc. In our approach, the number of sentences present in our summary would be equal to the number of paragraphs present in a text document, which can be achieved by using our successive threshold approach.

With the current trend in lieu of the ongoing pandemic, online learning and technology-based pedagogy receive worldwide attention. There are several pedagogical practices one can use to evaluate students mindful and the method will vary based on the learning objective. In this paper, our goal is to dive deeper into identifying the best keyword extraction technique which can be closely mapped with human evaluation. We report the results of three popular keyword extraction techniques specifically RAKE, TF-IDF, and Semantic Fingerprinting on the dataset generated from the discussion board post of a Learning Management System. The results illustrate that the TF-IDF algorithm shows the highest correlation with a human evaluation with 0.76 correlations, RAKE with 0.36, and Semantic Fingerprinting with 0.58. It was also identified Semantic Fingerprinting had a lowest mean square error of 18.26.

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.

Living in our information society poses the challenge of having to deal with a plethora of information. While most content is represented through text, keyword extraction and visualization techniques allow the processing and adjustment of text presentation to the readers’ individual requirements and preferences. In this paper, we investigate four types of text visualizations and their feasibility to give readers an overview before they actually engage with a text: word clouds, highlighting, mind maps, and image collages. In a user study with 50 participants, we assessed the effects of such visualizations on reading comprehension, reading time, and subjective impressions. Results show that (1) mind maps best support readers in getting the gist of a text, (2) they also give better subjective impressions on text content and structure, and (3) highlighting keywords in a text before reading helps to reduce reading time. We discuss a set of guidelines to inform the design of automated systems for creating text visualizations for reader support.

This study discusses an approach for abstractive text summarization in the Assamese language. Objective: The main objective of this paper is to develop a novel approach for abstractive text summarization in Assamese that efficiently condenses large information while keeping its core meaning. Methods: We utilise a sequence-to-sequence Recurrent Neural Network [RNN] model with an encoder-decoder architecture in this paper. In this study, we use a Bi-LSTM on the encoder side, an attention mechanism, a softmax layer on the decoder side, and Assamese news items obtained from renowned daily newspapers in Assam. Findings: The results show that the suggested model is effective, with a train loss of 0.008 and assessment scores based on ROUGE-1, ROUGE-2, and ROUGE-L criteria. Novelty: The novelty lies in filling the aforementioned gap by proposing and implementing an abstractive text summarization approach for the Assamese language. Improve Assamese text summarization approaches by applying the presented strategy to news items. Keywords: Abstractive, NLP, RNN, Seq2Seq, Text summarization

Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.

Automatic Text Summarization techniques in Assamese language is still in an immature stage compared to other Indian languages. In this study, we have reviewed some of the studies in other Indian languages for having a better understanding of the problems. Furthermore, we have also put in our efforts to understand keeping in view the opinion of Assamese linguists as how we could overcome the shortcomings of Assamese Text Summarization and provide an effective solution that could serve as fundamental concepts in this particular field. An extractive method for text summarization is proposed in this paper to evaluate the sentences of the input document based on the merging of semantic and statistical characteristics. It is applied considering the importance of sentence and diversity. Moreover, we have applied Synsets from Assamese WordNet to calculate the word frequency. Besides identifying and weighing the salience sentences, the concept of vector centrality and cosine similarity is used and the result is represented in graph representation. A threshold value to indicate the important sentences is applied. The effectiveness of our proposed method is demonstrated through a set of experiments conducted using ROUGE measure and the evaluation is showed in terms of Recall.

As a typical unsupervised learning method, the TextRank algorithm performs well for large-scale text mining, especially for automatic summarization or keyword extraction. However, TextRank only considers the similarities between sentences in the processes of automatic summarization and neglects information about text structure and context. To overcome these shortcomings, the authors propose an improved highly-scalable method, called iTextRank. When building a TextRank graph in their new method, the authors compute sentence similarities and adjust the weights of nodes by considering statistical and linguistic features, such as similarities in titles, paragraph structures, special sentences, sentence positions and lengths. Their analysis shows that the time complexity of iTextRank is comparable with TextRank. More importantly, two experiments show that iTextRank has a higher accuracy and lower recall rate than TextRank, and it is as effective as several popular online automatic summarization systems.