Recall-Oriented Understudy For Gisting Evaluation Metrics Research Articles

Video Transcripts Summarization using OpenAI Whisper and GPT Model

Abstract: In today’s digital age, a vast amount of video content is generated and shared on the internet every minute. However, extracting relevant information from these videos can be time-consuming and challenging. This is where video transcript summarization comes in, providing a concise summary of video content without the need to watch the entire video. The video transcript summarization system aims to streamline the process of extracting key insights and information from video content by generating concise and informative summaries from their transcripts. In the dynamic landscape of video content, existing approaches to transcript summarization have encountered challenges that limit their effectiveness. Issues such as compromised accuracy, prolonged processing times and a restricted focus on YouTube's captioned videos have prompted the exploration of alternative solutions. This paper introduces a novel approach that tackles these limitations head-on, employing innovative technologies to redefine the landscape of video transcript summarization. To address the identified limitations, we propose an alternative strategy that hinges on the utilization of advanced technologies like OpenAI Whisper Model Automatic Speech Recognition(ASR) for video’s Transcription and Generative Pre-trained Transformer (GPT) model for Summarization. Through the integration of the OpenAI Whisper model for transcription and GPT models for summarization, our proposed approach strives to redefine standards in accuracy, processing efficiency, multilingual support, and exhibit the capability to generate either extractive or abstractive summaries. In the context of natural language processing (NLP) and text summarization ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics will be used to evaluate the quality of generated summaries compared to human referenced summaries

Ar-CM-ViMETA: Arabic Image Captioning based on Concept Model and Vision-based Multi-Encoder Transformer Architecture

Image captioning is a major artificial intelligence research field that involves visual interpretation and linguistic description of a corresponding image. Successful image captioning relies on acquiring as much information as feasible from the original image. One of these essential bits of knowledge is the topic or the concept that the image is associated with. Recently, concept modeling technique has been utilized in English image captioning for completely capturing the image contexts and make use of these contexts to produce more accurate image descriptions. In this paper, a concept-based model is proposed for Arabic Image Captioning (AIC). A novel Vision-based Multi-Encoder Transformer Architecture (ViMETA) is proposed for handling the multi-outputs result from the concept modeling technique while producing the image caption. BiLingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) standard metrics have been used to evaluate the proposed model using the Flickr8K dataset with Arabic captions. Furthermore, qualitative analysis has been conducted to compare the produced captions of the proposed model with the ground truth descriptions. Based on the experimental results, the proposed model outperformed the related works both quantitatively, using BLEU and ROUGE metrics, and qualitatively

Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization

The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurdle due to the computational intensity of the process, particularly when employing complex optimization techniques like swarm intelligence optimization alongside a costly ATS repair operator. While the current approach yields impressive Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics for the generated summary, it struggles with inefficiencies, mainly attributed to the substantial optimization time consumed by the ATS repair operator scheme. In order to address this, a novel solution called Decomposition-based Multi-Objective Differential Evolution (MODE/D) is proposed. It is built upon the foundation of Differential Evolution for Multi-Objective Optimization (DEMO) and the weighted sum method (WS), coupled with an innovative ATS repair operator scheme. Through experimentation on Document Understanding Conferences (DUC) datasets, the novel approach of MODE/D – WS is validated by evaluating the results using ROUGE metrics. The outcomes are twofold: a remarkable reduction in serial execution time and a noteworthy enhancement over existing techniques in the scholarly domain, as evidenced by improved ROUGE-1, ROUGE-2, and ROUGE-L scores.

Automatic Arabic Text Summarization Using Analogical Proportions

Automatic text summarization is the process of generating or extracting a brief representation of an input text. There are several algorithms for extractive summarization in the literature tested by using English and other languages datasets; however, only few extractive Arabic summarizers exist due to the lack of large collection in Arabic language. This paper proposes and assesses new extractive single-document summarization approaches based on analogical proportions which are statements of the form “a is to b as c is to d”. The goal is to study the capability of analogical proportions to represent the relationship between documents and their corresponding summaries. For this purpose, we suggest two algorithms to quantify the relevance/irrelevance of an extracted keyword from the input text, to build its summary. In the first algorithm, the analogical proportion representing this relationship is limited to check the existence/non-existence of the keyword in any document or summary in a binary way without considering keyword frequency in the text, whereas the analogical proportion of the second algorithm considers this frequency. We have assessed and compared these two algorithms with some language-independent summarizers (LexRank, TextRank, Luhn and LSA (Latent Semantic Analysis)) using our large corpus ANT (Arabic News Texts) and a small test collection EASC (Essex Arabic Summaries Corpus) by computing ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (BiLingual Evaluation Understudy) metrics. The best-achieved results are ROUGE-1 = 0.96 and BLEU-1 = 0.65 corresponding to educational documents from EASC collection which outperform the best LexRank algorithm. The proposed algorithms are also compared with three other Arabic extractive summarizers, using EASC collection, and show better results in terms of ROUGE-1 = 0.75 and BLEU-1 = 0.47 for the first algorithm, and ROUGE-1 = 0.74 and BLEU-1 = 0.49 for the second one. Experimental results show the interest of analogical proportions for text summarization. In particular, analogical summarizers significantly outperform three among four language-independent summarizers in the case of BLEU-1 for ANT collection and they are not significantly outperformed by any other summarizer in the case of EASC collection.

A Text Abstraction Summary Model Based on BERT Word Embedding and Reinforcement Learning

As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis we propose a novel hybrid model of extractive-abstractive to combine BERT (Bidirectional Encoder Representations from Transformers) word embedding with reinforcement learning. Firstly, we convert the human-written abstractive summaries to the ground truth labels. Secondly, we use BERT word embedding as text representation and pre-train two sub-models respectively. Finally, the extraction network and the abstraction network are bridged by reinforcement learning. To verify the performance of the model, we compare it with the current popular automatic text summary model on the CNN/Daily Mail dataset, and use the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics as the evaluation method. Extensive experimental results show that the accuracy of the model is improved obviously.

Quantifying the informativeness for biomedical literature summarization: An itemset mining method

ObjectiveAutomatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our summarizer quantifies the informativeness of each sentence using the support values of itemsets appearing in the sentence. MethodsTo address the concept-level analysis of text, our method initially maps the original document to biomedical concepts using the Unified Medical Language System (UMLS). Then, it discovers the essential subtopics of the text using a data mining technique, namely itemset mining, and constructs the summarization model. The employed itemset mining algorithm extracts a set of frequent itemsets containing correlated and recurrent concepts of the input document. The summarizer selects the most related and informative sentences and generates the final summary. ResultsWe evaluate the performance of our itemset-based summarizer using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a set of experiments. We compare the proposed method with GraphSum, TexLexAn, SweSum, SUMMA, AutoSummarize, the term-based version of the itemset-based summarizer, and two baselines. The results show that the itemset-based summarizer performs better than the compared methods. The itemset-based summarizer achieves the best scores for all the assessed ROUGE metrics (R-1: 0.7583, R-2: 0.3381, R-W-1.2: 0.0934, and R-SU4: 0.3889). We also perform a set of preliminary experiments to specify the best value for the minimum support threshold used in the itemset mining algorithm. The results demonstrate that the value of this threshold directly affects the accuracy of the summarization model, such that a significant decrease can be observed in the performance of summarization due to assigning extreme thresholds. ConclusionCompared to the statistical, similarity, and word frequency methods, the proposed method demonstrates that the summarization model obtained from the concept extraction and itemset mining provides the summarizer with an effective metric for measuring the informative content of sentences. This can lead to an improvement in the performance of biomedical literature summarization.