A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods

H **, Y Zhang, D Meng, J Wang, J Tan - arxiv preprint arxiv:2403.02901, 2024‏ - arxiv.org
Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …

Multi-document summarization via deep learning techniques: A survey

C Ma, WE Zhang, M Guo, H Wang, QZ Sheng - ACM Computing Surveys, 2022‏ - dl.acm.org
Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …

Bartscore: Evaluating generated text as text generation

W Yuan, G Neubig, P Liu - Advances in neural information …, 2021‏ - proceedings.neurips.cc
A wide variety of NLP applications, such as machine translation, summarization, and dialog,
involve text generation. One major challenge for these applications is how to evaluate …

Graph neural networks for natural language processing: A survey

L Wu, Y Chen, K Shen, X Guo, H Gao… - … and Trends® in …, 2023‏ - nowpublishers.com
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

QMSum: A new benchmark for query-based multi-domain meeting summarization

M Zhong, D Yin, T Yu, A Zaidi, M Mutuma, R Jha… - arxiv preprint arxiv …, 2021‏ - arxiv.org
Meetings are a key component of human collaboration. As increasing numbers of meetings
are recorded and transcribed, meeting summaries have become essential to remind those …

Graph Relearn Network: Reducing performance variance and improving prediction accuracy of graph neural networks

Z Huang, K Li, Y Jiang, Z Jia, L Lv, Y Ma - Knowledge-Based Systems, 2024‏ - Elsevier
Recent studies show that the predictive performance of graph neural networks (GNNs) is
inconsistent and varies across different experimental runs, even with identical parameters …

Extractive summarization as text matching

M Zhong, P Liu, Y Chen, D Wang, X Qiu… - arxiv preprint arxiv …, 2020‏ - arxiv.org
This paper creates a paradigm shift with regard to the way we build neural extractive
summarization systems. Instead of following the commonly used framework of extracting …

Extractive summarization via chatgpt for faithful summary generation

H Zhang, X Liu, J Zhang - arxiv preprint arxiv:2304.04193, 2023‏ - arxiv.org
Extractive summarization is a crucial task in natural language processing that aims to
condense long documents into shorter versions by directly extracting sentences. The recent …

DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization

A Joshi, E Fidalgo, E Alegre… - Expert Systems with …, 2023‏ - Elsevier
In this paper, we propose DeepSumm, a novel method based on topic modeling and word
embeddings for the extractive summarization of single documents. Recent summarization …

Re-evaluating evaluation in text summarization

M Bhandari, P Gour, A Ashfaq, P Liu… - arxiv preprint arxiv …, 2020‏ - arxiv.org
Automated evaluation metrics as a stand-in for manual evaluation are an essential part of
the development of text-generation tasks such as text summarization. However, while the …