Help | Advanced Search
Computer Science > Computation and Language
Title: text summarization using large language models: a comparative study of mpt-7b-instruct, falcon-7b-instruct, and openai chat-gpt models.
Abstract: Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment was performed with different hyperparameters and evaluated the generated summaries using widely accepted metrics such as the Bilingual Evaluation Understudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) Score, and Bidirectional Encoder Representations from Transformers (BERT) Score. According to the experiment, text-davinci-003 outperformed the others. This investigation involved two distinct datasets: CNN Daily Mail and XSum. Its primary objective was to provide a comprehensive understanding of the performance of Large Language Models (LLMs) when applied to different datasets. The assessment of these models' effectiveness contributes valuable insights to researchers and practitioners within the NLP domain. This work serves as a resource for those interested in harnessing the potential of LLMs for text summarization and lays the foundation for the development of advanced Generative AI applications aimed at addressing a wide spectrum of business challenges.
Submission history
Access paper:.
- Other Formats
References & Citations
- Google Scholar
- Semantic Scholar
BibTeX formatted citation
Bibliographic and Citation Tools
Code, data and media associated with this article, recommenders and search tools.
- Institution
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .
An overview of Text Summarization techniques
Ieee account.
- Change Username/Password
- Update Address
Purchase Details
- Payment Options
- Order History
- View Purchased Documents
Profile Information
- Communications Preferences
- Profession and Education
- Technical Interests
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
Subscribe to the PwC Newsletter
Join the community, add a new evaluation result row, text summarization.
406 papers with code • 34 benchmarks • 92 datasets
Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
Benchmarks Add a Result
Most implemented papers
Attention is all you need.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
Get To The Point: Summarization with Pointer-Generator Networks
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
Text Summarization with Pretrained Encoders
For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not).
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.
Big Bird: Transformers for Longer Sequences
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Fastformer: Additive Attention Can Be All You Need
In this way, Fastformer can achieve effective context modeling with linear complexity.
A Deep Reinforced Model for Abstractive Summarization
We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).
Unified Language Model Pre-training for Natural Language Understanding and Generation
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
WikiHow: A Large Scale Text Summarization Dataset
Wikidepia/indonesia_dataset • 18 Oct 2018
Sequence-to-sequence models have recently gained the state of the art performance in summarization.
IMAGES
VIDEO
COMMENTS
The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance. To quickly have an overview of abundant information, Text Summarization (TS) comes into play. TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge. Over the decades Text Summarization techniques have been ...
The size of data on the Internet has risen in an exponential manner over the past decade. Thus, the need for a solution emerges, that transforms this vast raw information into useful information which a human brain can understand. One such common technique in research that helps in dealing with enormous data is text summarization. Automatic summarization is a renowned approach which is used to ...
Text summarization produces a summary of a document by highlighting its most important content ideas. Researchers have been developing text summarization techniques since the 1950s. Most summarization deals with summaries of single documents, but recent summarization efforts have also produced summaries from clusters of documents. This paper reviews recent approaches in three categories ...
Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts.
IBM Journal of Research and Development, 2(4), 354-361. Digital Library. ... Paper presented at the Text summarization branches out: Proceedings of the ACL-04 workshop. Google Scholar [13] ... IEEE transactions on neural networks, 2(6), 568-576. Digital Library. Google Scholar
The webpage is a leading paper journal and conferencing site suitable for reviewing this text summarization research. To get papers that fit the topic is to enter the following keywords or synonyms of the keywords determined from the research topic being conducted. ... A fuzzy ontology and its application to news summarization. IEEE Trans. Syst ...
In this paper, we have reviewed papers from IEEE and ACM libraries those related to Automatic Text Summarization for the English language. Steps of extractive text summarization Distribution of ...
Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and ...
Text Summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information precisely in large text in a short time with little efforts. Text summarization approaches are classified into two categories: extractive and abstractive ...
Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form. There are different approaches to text summarization, including ...