Top Research Papers on Text Summarization

This paper studies the development of a web application which summarizes the given input text using different models and its deployment and provides a gist of the corpus enabling comprehension in a timely manner.

A new approach to collecting abstract data using artificial neural networks (GANs), a class of deep learning models known for their ability to create patterns of real information, shows its promise in paving the way for advanced applications in fields.

The proposed model used a combination of unsupervised LSTM and TF-IDF techniques, which is the two-step summarization method for Kannada text summarization, and yielded 94% accurate results.

: In this new era

A two-fold approach to fetch the subject matter of videos through effective summarization using an Automatic Speech Recognition system based on a Convolutional Neural Network and Extractive Text Summarization.

Overall, it is found that the multi-LLM summarization approaches significantly outperform the baselines that leverage only a single LLM by up to 3x, indicating the effectiveness of multi-LLM approaches for summarization.

This survey intends to investigate some of the most relevant approaches both in the areas of single-document and multiple document summarizations, giving special prominence to experimental methods and extractive techniques.

A modification to the attention mechanism of the transformer model with pointer-generator layer is proposed, where attention mechanism receives frequency information for each word, which helps to boost rare words in a news summarization task.

Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness and readability. These rewriting systems consider extracted summaries as the only input, which is relatively focused but can lose important background knowledge. In this paper, we investigate contextualized rewriting, which ingests the entire original document. We formalize contextualized rewriting as a seq2seq problem with group alignments, introducing group tag as a solution to model the alignments, identifying extr...

Experimental results on a corpus of scientific articles show that extractive summarization benefits from using a highly accurate segmentation method, and concludes that segmentation helps in reducing the lead bias problem.

LaQSum is introduced, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms, and robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.

These are the first extractive and abstractive summarization systems for Hungarian, and the Hungarian monolingual models outperformed the multilingual BERT model in all cases.

This research proposed an abstractive text summarization model that gets data from source data or other documents and two summaries of this are generated, one from a philologist and the other by proposed model.

This study provides an extensive examination of G PT-based models, especially GPT-4, in their ability to effectively condense text and investigates the architectural elements, training approaches, and multiple strategies used by the model to generate high-quality summaries.

This paper delves into the fascinating realm of text summarization, a process that distills lengthy content into shorter, more manageable versions, like a shortcut for understanding lengthy documents without reading every word.

A novel framework is presented to facilitate users in extracting summaries and keywords from long texts at real-time that uses a hybrid approach based on feature extraction and unsupervised learning to generate quality summaries.

REVISE empowers users to create high-quality, personalized summaries by effectively harnessing both human expertise and AI capabilities, ultimately transforming the summarization process into a truly collaborative and adaptive experience.

This Online PDF to Audio Converter and Translator was created by using Python (Django) can instantly convert any PDF text into audio and along with reading any PDF document out loud, this application can also translate and vocalize any text into up to five languages.

This work aims to explore different techniques of text summarization and evaluate them on different parameters such as the extent of compression/summarization, retention of meaning/gist, and grammatical errors.

The occams package is written in Python and provides an easy-to-use modular interface, allowing it to work in conjunction with popular Python NLP packages, such as nltk, stanza or spacy.

This paper reviews text summarization approaches and recent deep learning models for this approach, and focuses on existing datasets for these approaches, which are reviewed, along with their characteristics and limitations.

This work defines a new learning objective for extractive summarization which incorporates learning signals from multiple oracle summaries and proves it is equivalent to estimating the oracle expectation for each document sentence.

A novel character-level neural architecture for extractive text summarization is proposed, with the model size reduced by 99.64% to 97.98% to make it suitable for deployment on-device such as in mobiles, tabs and smart speakers.

Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings.

Experimental results indicate that ChatGPT generally outperforms previous evaluation metrics across the three tasks, indicating its great potential for factual inconsistency evaluation, but a closer inspection ofChatGPT's output reveals certain limitations including its preference for more lexically similar candidates, false reasoning, and inadequate understanding of instructions.

This paper proposes SummIt, an iterative text summarization framework based on large language models like ChatGPT, which enables the model to refine the generated summary iteratively through self-evaluation and feedback, resembling humans' iterative process when drafting and revising summaries.

This work proposes the first effective query strategy for AL in ATS based on diversity principles and shows that given a certain annotation budget, using this strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores.

This paper has scrapped paragraphs from various Somali sources and summarized the text using Extractive Text Summarization Techniques to create an extractive text summarization for Somali language.

A small dataset of Tibetan text summarization is artificially constructed in this paper, which is composed of 1,000 real Tibetan news articles, each with a short summary, to promote the development of Tibetan informatization.

The purpose of this paper is to introduce the Julia programming language with a concentration on Text Summarization, an extractive summarization algorithm used for summarizing.

This paper investigates record summarization in two ways i.e., Abstract and Extract text summarization.

This work proposes a training objective for abstractive summarization based on rejection learning, in which the model learns whether or not to reject potentially noisy tokens, and proposes a regularized decoding objective that penalizes non-factual candidate summaries during inference by using the rejection probability learned during training.

This work analyzes the training dynamics for generation models, focusing on summarization, and finds that a propensity to copy the input is learned early in the training process consistently across all datasets studied.

A model is made to get better result in text summarization with Genism library in NLP that improves the overall meaning of the phrase and the person reading it can understand in a better way.

This work empirically investigated three kinds of locality in text summarization at different levels of granularity, ranging from sentences to documents, and shows that its model has a better performance compared with strong baseline models with efficient attention modules.

This work proposes a fine-tuned Text-to-Text Transformer (T5) to summarize medical reports and trains and test the model on the publicly available Indiana Dataset, and evaluates it using the ROUGE set of metrics.

This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey, and there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.

An exclusive survey on different methods, approaches of automatic text summarization which are published in different articles in most recent three years is performed.

This paper is focusing on detection of handwritten text and summarizing the document for which it found a better result with some methods which are clearly explained in this paper.

This study implements and compares the performance of various automatic summarization methods in order to gain insight into how long the methods take to implement and how accurate and human-like the generated summaries are.

The BART model, a concept of the Deep Learning model used for text summarization which is called BART (Bidirectional and Auto- Regressive Transformer), is compared with BERT, T5, and Roberta.

A multilingual Automated Text Summarization (ATS) method targeting the Financial Narrative Summization Task (FNS-2022) is proposed, with the sequence-to-sequence variant ranked 1st on ROUGE-2 F1 score on the test set for each of the three languages.

This paper develops a conversational chatbot that can conduct text summarization utilising innovative NLP approaches and uses six different algorithms: LexRank, TextRank, Latent Semantic Analysis (LSA), T5, BART and PEGASUS to produce a summary.

We describe the outline of Text Summarization Challenge (TSC hereafter), a text summarization evaluation conducted as one of the tasks at the NTCIR Workshop2. First, we introduce TSC explaining its background and purpose. Then we describe briefly types of summarization and summarization evaluation methods in general. Next, we focus on TSC, including the participants, the three tasks in TSC, data used, evaluation methods for each task, brief report on the results as well as the features of the Challenge . The future directions for TSC are mentioned at the end as conclusion.

This study proposes a text summarization method based on the Text-to- Text Transfer Transformer (T5) model, which manually created human summaries for the ten most useful reviews of a particular drug for 500 different drugs from the UCI drug reviews dataset.

Lotus (shorthand for Latent Prompt Tuning for Summarization), which is a single model that can be applied in both controlled and uncontrolled modes, and which consistently improves upon strong summarization models across four different summarization datasets.

This research analysis uses the BBC news dataset to evaluate and compare the results obtained from the machine learning models and transformer architecture-based pre-trained models.

This research project is focused mainly on abstractive text summarization that extracts the most important contents from a text in a rephrased form and used the ROUGE-1 metric to analyze the quality of the text summarizations.

A survey of automatic text summarization techniques and a model which utilizes BERT and Group Average Linkage clustering on articles taken from PubMed is proposed, which would provide a brief summary by condensing the information in a nutshell, thus saving time.

This work builds an abstract text summarizer model for the Arabic language content using the state-of-the-art “Transformer” language model and proposes an iterative data augmentation technique which uses synthetic in-domain data along with the real summarization in- domain data for Arabic.