Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review

• URL: http://arxiv.org/abs/2310.09411v1
• Date: Fri, 13 Oct 2023 21:24:37 GMT
• Title: Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review
• Authors: Guanghua Wang, Weili Wu
• Abstract summary: Deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks.
• Score: 2.4185510826808487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting.

Related papers

• A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models [0.8899670429041453]
  We show that generative large language models (LLMs) can solve NLP tasks with very high quality without the need for extensive data. Based on a novel prompting strategy, we show that LLMs are able to outperform state-of-the-art machine learning approaches.
  arXiv  Detail & Related papers  (2024-07-26T06:39:35Z)
• Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
  Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
  arXiv  Detail & Related papers  (2024-07-04T16:41:08Z)
• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering [70.6359636116848]
  We propose a new large-scale dataset, ConvFinQA, to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations.
  arXiv  Detail & Related papers  (2022-10-07T23:48:50Z)
• Leveraging Natural Supervision for Language Representation Learning and Generation [8.083109555490475]
  We describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision. We first investigate self-supervised training losses to help enhance the performance of pretrained language models for various NLP tasks. We propose a framework that uses paraphrase pairs to disentangle semantics and syntax in sentence representations.
  arXiv  Detail & Related papers  (2022-07-21T17:26:03Z)
• Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
  Deep learning has been the mainstream technique in natural language processing (NLP) area. Deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
  arXiv  Detail & Related papers  (2022-05-03T13:58:38Z)
• Federated Learning Meets Natural Language Processing: A Survey [12.224792145700562]
  Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.
  arXiv  Detail & Related papers  (2021-07-27T05:07:48Z)
• An Empirical Survey of Data Augmentation for Limited Data Learning in NLP [88.65488361532158]
  dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks. Data augmentation methods have been explored as a means of improving data efficiency in NLP. We provide an empirical survey of recent progress on data augmentation for NLP in the limited labeled data setting.
  arXiv  Detail & Related papers  (2021-06-14T15:27:22Z)
• Pretrained Language Models for Text Generation: A Survey [46.03096493973206]
  We present an overview of the major advances achieved in the topic of pretrained language models (PLMs) for text generation. We discuss how to adapt existing PLMs to model different input data and satisfy special properties in the generated text.
  arXiv  Detail & Related papers  (2021-05-21T12:27:44Z)
• Low-Resource Adaptation of Neural NLP Models [0.30458514384586405]
  This thesis investigates methods for dealing with low-resource scenarios in information extraction and natural language understanding. We develop and adapt neural NLP models to explore a number of research questions concerning NLP tasks with minimal or no training data.
  arXiv  Detail & Related papers  (2020-11-09T12:13:55Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.