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A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods
Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …
Multi-document summarization via deep learning techniques: A survey
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 …
generates an informative and concise summary from a cluster of topic-related documents …
Bartscore: Evaluating generated text as text generation
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 …
involve text generation. One major challenge for these applications is how to evaluate …
Graph neural networks for natural language processing: A survey
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 …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
QMSum: A new benchmark for query-based multi-domain meeting summarization
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 …
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
Recent studies show that the predictive performance of graph neural networks (GNNs) is
inconsistent and varies across different experimental runs, even with identical parameters …
inconsistent and varies across different experimental runs, even with identical parameters …
Extractive summarization as text matching
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 …
summarization systems. Instead of following the commonly used framework of extracting …
Extractive summarization via chatgpt for faithful summary generation
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 …
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
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 …
embeddings for the extractive summarization of single documents. Recent summarization …
Re-evaluating evaluation in text summarization
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 …
the development of text-generation tasks such as text summarization. However, while the …