Important Sentences Research Articles

Revamping Image-Recipe Cross-Modal Retrieval with Dual Cross Attention Encoders

The image-recipe cross-modal retrieval task, which retrieves the relevant recipes according to food images and vice versa, is now attracting widespread attention. There are two main challenges for image-recipe cross-modal retrieval task. Firstly, a recipe’s different components (words in a sentence, sentences in an entity, and entities in a recipe) have different weight values. If a recipe’s different components own the same weight, the recipe embeddings cannot pay more attention to the important components. As a result, the important components make less contribution to the retrieval task. Secondly, the food images have obvious properties of locality and only the local food regions matter. There are still difficulties in enhancing the discriminative local region features in the food images. To address these two problems, we propose a novel framework named Dual Cross Attention Encoders for Cross-modal Food Retrieval (DCA-Food). The proposed framework consists of a hierarchical cross attention recipe encoder (HCARE) and a cross attention image encoder (CAIE). HCARE consists of three types of cross attention modules to capture the important words in a sentence, the important sentences in an entity and the important entities in a recipe, respectively. CAIE extracts global and local region features. Then, it calculates cross attention between them to enhance the discriminative local features in the food images. We conduct the ablation studies to validate our design choices. Our proposed approach outperforms the existing approaches by a large margin on the Recipe1M dataset. Specifically, we improve the R@1 performance by +2.7 and +1.9 on the 1k and 10k testing sets, respectively.

Fast Hybrid Approach for Thai News Summarization

News summarization presents a significant challenge in Natural Language Processing (NLP). Lengthy news articles not only consume valuable time but also lead to confusion regarding key points. The ideal news summarization should swiftly produce a succinct summary while retaining the essence of the information conveyed by the news writer. While intelligent chatbots like ChatGPT and Gemini offer user-friendly text summarization, their embedded Large Language Model (LLM) cannot be downloaded for private use. Moreover, implementing them in a business process can be expensive, both in terms of pay-per-use costs and response time. The objective of this research is to develop a private Thai news summarization that effectively extracts sentences encapsulating the main idea and abstractly summarizes them. The proposed model consists of two components. The first extracts a contiguous region containing important sentences using the TextRank algorithm, while the second employs the finetuned mBART as an LLM to generate the abstractive summary from the previously extracted sentences. In other words, the proposed model extracts an important news region before passing it to mBART. This approach produces a news summary with key information and a syntactic style akin to the natural Thai language. We evaluate the summarization quality by ROUGE scores and BERTScore (precision, recall, and F1-score). The evaluation metrics Experimental results on the ThaiSum dataset show relatively high ROUGE scores and BERTScore for the proposed model compared to most of the other approaches. Furthermore, it significantly reduces the runtime, keeping it within a reasonable limit.

PROCESSAMENTO DE LINGUAGEM NATURAL PARA RECONHECIMENTO DE ENTIDADES NOMEADAS EXTRAÍDAS DE CORPUS

This article presents a code implementation to automate the reading, analysis and generation of summaries of unstructured web texts, highlighting the crucial role of Natural Language Processing (NLP). The process was divided into two stages: data collection and pre-processing. In the first stage, a scientific article was selected and relevant data was extracted via web scraping, organized into an HTML page hosted online. In pre-processing, tokenization, normalization, stopword removal and word counting were performed. Using NLTK, the most important sentences were identified and ranked based on keyword frequency, allowing the selection of the most relevant sentences from the introduction, methodology, results/dicussion and conclusion sections. Discrepancies such as links and figure descriptions have been removed to improve the clarity of the abstract. The “most_common()” method was used to select the most relevant words in each section. After additional processing to delete unnecessary words, the final summary was clear and understandable, despite minor flaws. This automated approach provides an efficient way to synthesize information from scientific articles, optimizing the textual analysis process.

Comparative Analysis of Semantic and Syntactic Approaches in Automatic Text Summarization: A Comprehensive Review and Evaluation

It is very important to use automatic text summarization to turn long chunks of text into short, useful snippets. In the area of automatic text summary, this study looks at both semantic and syntactic methods in great detail. Using methods like natural language understanding and semantic processing, semantic approaches try to get meaning and context from writing. These methods try to get at the main ideas and connections in the material so that recaps can be more complex and full of information. Syntactic methods, on the other hand, focus on the structure of language, such as grammar and syntax. To find important sentences and phrases for summarization, methods like sentence extraction based on grammar patterns and grammatical analysis are used. Syntactic methods often make sure that the outlines they create are grammatically right and make sense. This review compares both methods in a number of areas, such as how well they capture the spirit of the original text, how well the generated snippets make sense, how quickly they can be produced, and how well they can be used with different types of material. The pros and cons of each method are shown in comparative studies, which help us understand how they can be used in various areas and situations. Recent improvements that combine semantic and syntactic methods have also shown promise in making automatic recaps better overall and more helpful. The goal of these mixed methods is to make reports that are both verbally and semantically correct by using the benefits of both semantic understanding and syntactic structure. This review gives us a more complete picture of how automatic text summarization methods are changing. It stresses how important it is to look at both semantic and syntactic approaches in order to make the field move forward and create better and more advanced summary systems.

Graphs in clusters: a hybrid approach to unsupervised extractive long document summarization using language models

Effective summarization of long documents is a challenging task. When addressing this challenge, Graph and Cluster-Based methods stand out as effective unsupervised solutions. Graph-Based Unsupervised methods are widely employed for summarization due to their success in identifying relationships within documents. Cluster-Based methods excel in minimizing redundancy by grouping similar content together before generating a concise summary. Therefore, this paper merges Cluster-Based and Graph-Based methods by applying language models for Unsupervised Extractive Summarization of long documents. The approach simultaneously extracts key information while minimizing redundancy. First, we use BERT-based sentence embeddings to create sentence clusters using k-means clustering and select the optimum number of clusters using the elbow method to ensure that sentences are categorized based on their semantic similarities. Then, the TextRank algorithm is employed within each cluster to rank sentences based on their importance and representativeness. Finally, the total similarity score of the graph is used to rank the clusters and eliminate less important sentence groups. Our method achieves comparable or better summary quality and reduced redundancy compared to both individual Cluster-Based and Graph-Based methods, as well as other supervised and Unsupervised baseline models across diverse datasets.

Next-Gen Linguistic Intelligence Image Summarizer for Documents Using Attention Model

Abstract: The process of producing a succinct synopsis of a text involves two primary techniques: both abstractive and extractive While abstractive summarising rephrases text in a way that is more human-like, extractive summarization chooses important statements straight. handbook Because it takes time and money to summarise because it requires reading through big texts, an automated text model with lots of features It is suggested to use both extractive and abstractive methods for summarising. Important sentences that are enhanced by linguistic features are identified by a feature-rich extractor. These sentences are then further enhanced by an abstracter that adds components such named entity tags and part-of-speech tags. Moreover, a loss function is presented to match attentions at the word and sentence levels. At last the summarized text is converted into image by using parsing and other relatable techniques.

Representasi Nasihat Luqman al-Hakim dalam Animasi Upin dan Ipin Episode “Indahnya Rumah Rasa Sayang”

This study aims to discuss the representation of Luqman Hakim's advice in the animation Upin and Ipin episode "Beautiful Rumah Rasa Sayang": Perspective of the Living Qur'an. This research is a descriptive qualitative research based on literature sources. The data used in this study comes from observation and documentation of it. The purpose of this study is to firstly analyze the verbal messages in the series and Lukman's advice to his son in Surah Luqman 12-19. Second, discussing the application of the living Qur'an in popular culture, especially films. The results of the study explain that there are seven important sentences contained in the letter Lukman verses 12-19 which are implemented in this broadcast, namely 1) the command to always be grateful; 2) command always do good to parents; 3) explain all human actions, good and bad; 4) orders to pray, do what is good, prevent evil, and be patient with various things that happen to you; 5) giving a smile and a cheerful face and not looking away from other people because of pride; 6) modest in behavior and speech; 7) maintain good manners in interacting with others.

Beyond Extractive Methods – Navigating the landscape of Abstractive Summarization Methods

Today, millions of data are generated every hour, which highlights the need for summarizing all this data accurately and efficiently. Doing such a task manually is tedious. This welcomes the need for automatic summarizing techniques. Generating precise and concise summaries of long text data is a necessity. Automatic summarization includes two primary techniques- Extractive and Abstractive Summarization. Extractive Summarization uses important sentences and keywords to construct the summary whereas abstractive summarization understands the text and generates a summary. The encoder-decoder architecture is generally used for abstractive summarization. This study briefs about various transformer architectures, including T5, BART, and Pegasus. Furthermore, a comparative analysis of these models on the same data is presented and the result of the same is compared on scores with the manually generated summaries- ROUGE1, ROUGE2, and ROUGEL. The purpose of this study is to understand the advancement of abstractive text summarization models as well as to understand the strategies and their usefulness.

LeanContext: Cost-efficient domain-specific question answering using LLMs

Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, widespread LLM integration presents a challenge for small businesses due to the high expenses of LLM API usage. Costs rise rapidly when domain-specific data (context) is used alongside queries for accurate domain-specific LLM responses. Extracting context from domain-specific data is implemented by a Retrieval Augmented Generation (RAG) approach. One option is to summarize the RAG context by using LLMs and reduce the context. However, this can also filter out useful information that is necessary to answer some domain-specific queries. In this paper, we shift from human-oriented summarizers to AI model-friendly summaries. Our approach, LeanContext, efficiently extracts k key sentences from the context that are closely aligned with the query. The choice of k is neither static nor random; we introduce a reinforcement learning technique that dynamically determines k based on the query and context. The rest of the less important sentences are either reduced using a free open-source text reduction method or eliminated. We evaluate LeanContext against several recent query-aware and query-unaware context reduction approaches on prominent datasets (arxiv papers and BBC news articles, NarrativeQA). Despite cost reductions of 37.29% to 67.81%, LeanContext’s ROUGE-1 score decreases only by 1.41% to 2.65% compared to a baseline that retains the entire context (no summarization). LeanContext stands out for its ability to provide precise responses, outperforming competitors by leveraging open-source summarization techniques. Human evaluations of the responses further confirm and validate this superiority. Additionally, if open-source pre-trained LLM-based summarizers are used to reduce context (into human consumable summaries), LeanContext can further modify the reduced context to enhance the accuracy (ROUGE-1 score) by 13.22% to 24.61%.

TA-Sum: The Extractive Summarization Research Based on Topic Information

<p>Text summarization is divided into extractive summarization and abstractive summarization. The extractive summarization technology aims to extract some main phrases and sentences from the original text to form a short summary for people to read quickly. However, extractive summarization is faced with problems such as poor sentence coherence and incomplete information, which makes it difficult to screen out important sentences from the source text. DNN (Deep Neural Network) is widely used for text summarization task. This paper proposes a TA-Sum model based on the neural topic model. Introducing the topic information can help people understand the relevant main content of source text quickly. We obtain the topic information using the neural topic model and implement the attention mechanism to fuse the topic information with the text representation, which improves the semantic integrity and completeness of the summary. The experimental results on the large-scale English data sets CNN/Daily mail are improved by 0.37%, 0.11%, and 0.17% respectively compared with BertSum, which demonstrates the effectiveness of our method.</p> <p> </p>

A novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods

Multi Document Summarization (MDS) is a technique for extracting succinct summaries from groups of related documents. The usage of MDS in the e-learning context is more appealing for providing summaries for learning materials, which helps students and teachers to focus on key concepts of the learning materials. In a learning material, availability of non-textual document-elements such as figures/diagrams, plots, graphs, tables and algorithms can be seen widely, but keeping such document-elements in the summary is not possible as they are non-textual. This proposed work incorporates the text summarization approach with document-elements augmentation to the summary to provide a detailed coverage of information without exceeding the summary length constraint. The key information in the source text is identified by important phrase features and sentence features, and the summaries are generated by selecting important sentences using the Integer Linear Programming (ILP) framework while reducing the redundancy using pre-trained sentence vectors. The relationships between the summary and document-elements are identified through document-element snippet extraction and a Hierarchical Agglomerative Clustering approach. Experimental results of the proposed summary extraction and augmentation on educational dataset (EduSumm) show better performance compared to the state-of-the-art approaches.

NLP BASED TEXT SUMMARIZATION USING BART MODEL

Text summarization plays a major role which can be used to summarize articles, papers, documents, etc. It greatly helps the students, business executives, librarian, teachers and many people in different profiles to quickly get the gist of research papers, latest news, trends in industry, summarize books, articles, etc. Summarizing provides a concise overview of a document which helps us to make better decisions and communicate information more effectively. In this project we are going to implement an nlp based text summarization using bart model and finally deployed using django framework. Bart hugging face transformer model is one of the nlp models. Its input and output are in the form of sequence. The high- dimensional representation of the input is learnt by the encoder and then mapped to the decoder. The bart model is first pre- trained on a big text (like book corpus or wikipedia). Pretraining ensures that the model "understands the language" and has a solid foundation from which to learn and how to carry out further tasks. Since it will determine how successfully the model can be trained for tasks like text classification or text summarization, the model's capacity to understand language is more effective. The pre-trained weights and weights in the bart model are fine-tuned on question answering, text summarization, sequence classification, etc. Therefore, using bart model we can able to generate text in both extractive and abstractive way, by which we can get the simpler summarization with the important sentences and also more fluent and accurate summarization by understanding the meaning of the text. Finally, to provide a user-friendly interface, by which the users can easily use the text summarization, the project will be deployed using django framework. It allows us to easily build web applications. Key Words: BART, NLP, summarization, Wikipedia, pretrained

Generating survey draft based on closeness of position distributions of key words

Automatically generating a survey draft is a challenge to text summarization research because it needs to select important sentences from important references in a large set of candidate papers for composing sections that are in line with section titles and different sections discuss the most relevant reference papers of different number, which are beyond the capability of previous text summarization approaches as they assume that all candidate papers should be included into one summary. This paper proposes an approach to generating survey draft according to a pattern consisting of sections with titles given by the user who requests the survey. The problem of generating each section can be divided into the following sub-problems: (1) rank the input scientific documents (in short documents) according to the title of a section, (2) determine the number of documents that are most relevant to the title, and (3) rank and select sentences from the selected documents according to the title. A position closeness distance of key word is proposed to rank a set of documents by measuring how closely two key words within section title are distributed within each document, which is used to rank the documents. The rationale is that the positions of the neighboring key words of a section title should be closer in more relevant documents than other words. As different sections have different number of selected documents, a method is proposed to determine the number of documents to be included into the current section based on the slope shape of the sorted rank curve of documents according to the section title. Based on the duality property of the closeness, ranks of sentences within a document can be directly obtained when the document is ranked according to the title of section, and both the importance and coherence of selected sentences can be reflected without extra calculation for ranking sentences. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with other approaches. The proposed approach is significant in applications as different surveys can be generated according to different patterns given by different users.

Summarizing twitter posts regarding COVID-19 based on n-grams

The COVID-19 pandemic announced by the World Health Organization has disrupted human lives at different scales, including the economy, public health, and people's emotions. Social media databases record huge accumulated information concern this pandemic. Twitter platform is considered one of the most active social media that enable users to tweet in different conversations they are concerned about. The problem arises when tweeters want to search about a specific topic. They can only sort tweets by its recency to understand conversation and not by relevancy. This makes tweeters read through the most tweets to understand what was firstly discussed about the related topic. Some strategies were developed for summarizing tweets but summarizing topics of COVID-19 are still at the beginning. The current research aims to introduce a technique to present a short summary related COVID-19 topics with consuming little time and effort. Thus, summarization task started by clustering topics based on latent dirichlet allocation (LDA) method and K-means clustering and then selected the important sentences to format summarization. The study also compares bigram-based and unigram-based summarization. Different metrics were used to evaluate results and experiments at each stage, and the output of the proposal system was evaluated using ROUGE metrics.

A Multi Document Summarization of Learning Materials using Bigram Embedding Technique and Integer Linear Programming

In the present era of the Internet, teachers and learners are heavily inclined to use e-learning systems for an efficient learning process. Due to the proliferation of educational text contents in these e-learning systems, the need for incorporating advanced text analysis tools and techniques are becoming inevitable. Multi Document Summarization (MDS) is a technique for producing concise summaries from a collection of related text documents. The usage of MDS in the context of e-learning is more promising for providing summaries for learning materials which helps students and teachers to focus on key concepts of the learning materials. In this paper a semantic approach towards the learning material summarization is proposed based on bigram embedding and ILP technique. This approach considers bigram as the basic meaningful semantic unit of the sentences to understand and summarize documents. Embedding techniques are employed to learn the vector representation of phrases to semantically identify similar phrases to reduce the redundancy and improving coherence. Using ILP technique, the summaries were generated by selecting important sentences while reducing the redundancy using phrase vectors. Experimental results on newly created educational dataset (EduSumm) shows better performance compared to the baseline systems.

Arabic Extractive Summarization Using Pre-Trained Models

Automatic Text Summarization (ATS) is a crucial area of study in Natural Language Processing (NLP) due to the vast amount of online information available. Extractive summarization, which involves selecting important sentences from the original document without altering their wording, is one approach to generating summaries. While many methods for Arabic text summarization exist, deep learning applications are still in their early stages, and there is a shortage of available datasets. Unlike English, there have been fewer experiments conducted on Arabic language summarization due to its unique characteristics. This study aims to fill this gap by experimenting with several models for summarizing Arabic text, including QARiB, AraELECTRA, and AraBERT-base models, all trained using the KALIMA dataset. The AraBERT model performed exceptionally well, achieving high scores of 0.44, 0.26, and 0.44 on the ROUGE-1, ROUGE-2, and ROUGE-L measures, respectively.

Food safety news events classification via a hierarchical transformer model

In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance.

Abstractive vs. Extractive Summarization: An Experimental Review

Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the top-n most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSUM, BLEU-1, BLEU-2 and SACREBLEU; (iii) we report on insights gained on various aspects of the text summarization process, including existing approaches, datasets and evaluation methods, and we outline a set of open issues and future research directions; (iv) we upload the datasets and the code used in our experiments in a public repository, aiming to increase the reproducibility of this work and facilitate future research in the field.

Effective News Text Summarization Techniques

n proposed work, we successfully implemented a news text summarization system using Natural Language Processing (NLP) techniques and the Latent Semantic Analysis (LSA) algorithm. The purpose of our project was to extract important information from a large volume of news articles and present it in a concise and easily understandable manner. To achieve this, we utilized the LSA algorithm, which is known for its ability to capture the underlying semantic structure of text. LSA employs a mathematical model to analyse relationships between words in a document, creating a semantic representation where words with similar contexts are grouped together in a vector space. The LSA-based summarization process involved several steps. First, we pre-processed the news articles by removing stop words, punctuation, and other non-relevant elements. Then, we constructed a term-document matrix, where rows represented words and columns represented documents, with matrix values representing word frequencies. Next, we applied Singular Value Decomposition (SVD) to the term-document matrix. SVD helped reduce the matrix's dimensionality by identifying the most important latent semantic concepts. This resulted in a lower-dimensional representation that captured the essential information. Finally, we identified the most important sentences in the news articles by measuring the cosine similarity between each sentence and the summary. Sentences with the highest cosine similarity scores were selected as summary sentences. The proposed system demonstrated the effectiveness of the LSA algorithm for news text summarization. By capturing the semantic structure of the text, it generated summaries that allowed users to understand the key points of a news article quickly and easily. Our implementation had practical applications for content recommendation systems, news aggregation platforms, and personalized news feeds. However, it is important to acknowledge the limitations of the LSA algorithm. It may struggle with handling idiomatic expressions and can be sensitive to the quality of the input data. These considerations highlight the need for ongoing research and development to enhance the performance and robustness of news text summarization systems.

Het privilege van Filips de Goede aan de vrije schippers van Gent (1436)

In 1436, the free shippers received a written confirmation of their privileges from Philip the Good, following their help in suppressing a rebellion of tradesmen in Ghent. This contribution aims to explain the privileges of the free shippers in more detail through a substantive and formal analysis of the charter of 1436. One of the most important sentences in this charter turns out to be the most enigmatic as well: the free shipper from Ghent were said to have been ‘founded’ on the Scheldt, the Lieve and the Lys. This verb appears to imply that the free skippers had the right to exercise control over these waterways; moreover, the free shippers believed that their corporation had been created for the sole purpose of steering the economic traffic on these waterways. Supposedly, their privileges already existed as customary law, and this charter provided them with written proof.