Recent Trends in Unsupervised Summarization
- URL: http://arxiv.org/abs/2305.11231v2
- Date: Thu, 26 Sep 2024 15:32:47 GMT
- Title: Recent Trends in Unsupervised Summarization
- Authors: Mohammad Khosravani, Amine Trabelsi,
- Abstract summary: Unsupervised summarization is a powerful technique that enables training summarizing models without requiring labeled datasets.
This survey covers different recent techniques and models used for unsupervised summarization.
- Score: 0.6752538702870792
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised summarization is a powerful technique that enables training summarizing models without requiring labeled datasets. This survey covers different recent techniques and models used for unsupervised summarization. We cover extractive, abstractive, and hybrid models and strategies used to achieve unsupervised summarization. While the main focus of this survey is on recent research, we also cover some of the important previous research. We additionally introduce a taxonomy, classifying different research based on their approach to unsupervised training. Finally, we discuss the current approaches and mention some datasets and evaluation methods.
Related papers
- Generalized Video Anomaly Event Detection: Systematic Taxonomy and
Comparison of Deep Models [33.43062232461652]
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems.
This survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED)
arXiv Detail & Related papers (2023-02-10T07:11:37Z) - A New Sentence Extraction Strategy for Unsupervised Extractive
Summarization Methods [26.326800624948344]
We model the task of extractive text summarization methods from the perspective of Information Theory.
To improve the feature distribution and to decrease the mutual information of summarization sentences, we propose a new sentence extraction strategy.
arXiv Detail & Related papers (2021-12-06T18:00:02Z) - A Comparative Review of Recent Few-Shot Object Detection Algorithms [0.0]
Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem.
Recent studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions.
arXiv Detail & Related papers (2021-10-30T07:57:11Z) - Deep Learning Schema-based Event Extraction: Literature Review and
Current Trends [60.29289298349322]
Event extraction technology based on deep learning has become a research hotspot.
This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
arXiv Detail & Related papers (2021-07-05T16:32:45Z) - The Summary Loop: Learning to Write Abstractive Summaries Without
Examples [21.85348918324668]
This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint.
Key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary.
When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points.
arXiv Detail & Related papers (2021-05-11T23:19:46Z) - Abstractive Query Focused Summarization with Query-Free Resources [60.468323530248945]
In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system.
We propose Marge, a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries.
Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting.
arXiv Detail & Related papers (2020-12-29T14:39:35Z) - CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural
Summarization Systems [121.78477833009671]
We investigate the performance of different summarization models under a cross-dataset setting.
A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways.
arXiv Detail & Related papers (2020-10-11T02:19:15Z) - A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021.
We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
arXiv Detail & Related papers (2020-08-02T00:09:03Z) - Few-Shot Learning for Opinion Summarization [117.70510762845338]
Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents.
In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text.
Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
arXiv Detail & Related papers (2020-04-30T15:37:38Z) - Unsupervised Opinion Summarization with Noising and Denoising [85.49169453434554]
We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof.
At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise.
arXiv Detail & Related papers (2020-04-21T16:54:57Z)
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.