Abstractive summarization is a machine learning technique that generates concise summaries of text by creating new phrases and sentences, rather than simply extracting existing ones from the source material.
In recent years, neural abstractive summarization methods have made significant progress, particularly for single document summarization (SDS). However, challenges remain in applying these methods to multi-document summarization (MDS) due to the lack of large-scale multi-document summaries. Researchers have proposed approaches to adapt state-of-the-art neural abstractive summarization models for SDS to the MDS task, using a small number of multi-document summaries for fine-tuning. These approaches have shown promising results on benchmark datasets.
One major concern with current abstractive summarization methods is their tendency to generate factually inconsistent summaries, or 'hallucinations.' To address this issue, researchers have proposed Constrained Abstractive Summarization (CAS), which specifies tokens as constraints that must be present in the summary. This approach has been shown to improve both lexical overlap and factual consistency in abstractive summarization.
Abstractive summarization has also been explored for low-resource languages, such as Bengali and Telugu, where parallel data for training is scarce. Researchers have proposed unsupervised abstractive summarization systems that rely on graph-based methods and pre-trained language models, achieving competitive results compared to extractive summarization baselines.
In the context of dialogue summarization, self-supervised methods have been introduced to enhance the semantic understanding of dialogue text representations. These methods have contributed to improvements in abstractive summary quality, as measured by ROUGE scores.
Legal case document summarization presents unique challenges due to the length and complexity of legal texts. Researchers have conducted extensive experiments with both extractive and abstractive summarization methods on legal datasets, providing valuable insights into the performance of these methods on long documents.
To further advance the field of abstractive summarization, researchers have proposed large-scale datasets, such as Multi-XScience, which focuses on summarizing scientific articles. This dataset is designed to favor abstractive modeling approaches and has shown promising results with state-of-the-art models.
In summary, abstractive summarization has made significant strides in recent years, with ongoing research addressing challenges such as factual consistency, multi-document summarization, and low-resource languages. Practical applications of abstractive summarization include generating news summaries, condensing scientific articles, and summarizing legal documents. As the technology continues to improve, it has the potential to save time and effort for professionals across various industries, enabling them to quickly grasp the essential information from large volumes of text.
Abstractive Summarization
Abstractive Summarization Further Reading
1.Towards a Neural Network Approach to Abstractive Multi-Document Summarization http://arxiv.org/abs/1804.09010v1 Jianmin Zhang, Jiwei Tan, Xiaojun Wan2.A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization http://arxiv.org/abs/2204.09519v1 Meng Cao3.Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation http://arxiv.org/abs/2010.12723v2 Yuning Mao, Xiang Ren, Heng Ji, Jiawei Han4.Neural Abstractive Text Summarizer for Telugu Language http://arxiv.org/abs/2101.07120v1 Mohan Bharath B, Aravindh Gowtham B, Akhil M5.Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization http://arxiv.org/abs/2209.00278v1 Hyunjae Lee, Jaewoong Yun, Hyunjin Choi, Seongho Joe, Youngjune L. Gwon6.Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation http://arxiv.org/abs/2210.07544v1 Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh7.Unsupervised Abstractive Summarization of Bengali Text Documents http://arxiv.org/abs/2102.04490v2 Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat8.Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles http://arxiv.org/abs/2010.14235v1 Yao Lu, Yue Dong, Laurent Charlin9.Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information http://arxiv.org/abs/1810.06065v1 Lisa Fan, Dong Yu, Lu Wang10.Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization http://arxiv.org/abs/2109.08569v1 Ahmed Magooda, Diane LitmanAbstractive Summarization Frequently Asked Questions
What is abstractive text summarization in NLP?
Abstractive text summarization is a natural language processing (NLP) technique that aims to generate concise summaries of text by creating new phrases and sentences, rather than simply extracting existing ones from the source material. This approach allows for more coherent and informative summaries, as it can capture the main ideas and concepts in the original text while using fewer words and avoiding redundancy.
What is abstractive vs extractive summarization?
Abstractive and extractive summarization are two main approaches to text summarization. Extractive summarization involves selecting and combining the most important sentences or phrases from the original text to create a summary. In contrast, abstractive summarization generates new sentences and phrases that convey the main ideas of the source material, resulting in a more concise and coherent summary. While abstractive summarization can produce more natural and informative summaries, it is generally more challenging to implement due to the need for advanced NLP techniques and models.
How do neural abstractive summarization methods work?
Neural abstractive summarization methods leverage deep learning techniques, such as recurrent neural networks (RNNs), transformers, and attention mechanisms, to generate summaries. These models are trained on large-scale datasets containing pairs of source texts and their corresponding summaries. During training, the model learns to understand the semantic relationships between words and phrases in the text and generate new sentences that capture the main ideas. Once trained, the model can be used to generate abstractive summaries for new, unseen texts.
What are the challenges in multi-document summarization (MDS)?
Multi-document summarization (MDS) involves generating a single summary from multiple related documents. This task presents several challenges compared to single-document summarization (SDS), including: 1. Identifying and merging relevant information from multiple sources. 2. Handling redundancy and contradictions between different documents. 3. Ensuring coherence and logical flow in the generated summary. 4. Lack of large-scale multi-document summary datasets for training and evaluation. Researchers have been working on adapting state-of-the-art neural abstractive summarization models for SDS to the MDS task, using a small number of multi-document summaries for fine-tuning and achieving promising results on benchmark datasets.
How can factual consistency be improved in abstractive summarization?
Factual consistency is a major concern in abstractive summarization, as models may generate factually inconsistent summaries or 'hallucinations.' One approach to address this issue is Constrained Abstractive Summarization (CAS), which specifies tokens as constraints that must be present in the summary. By incorporating these constraints, the model is guided to generate summaries that are both lexically overlapping with the source text and factually consistent. Researchers have shown that CAS can improve the quality and accuracy of abstractive summaries.
What are some practical applications of abstractive summarization?
Abstractive summarization has a wide range of practical applications across various industries, including: 1. Generating news summaries: Quickly providing readers with the main points of news articles. 2. Condensing scientific articles: Helping researchers and professionals grasp the key findings and implications of scientific papers. 3. Summarizing legal documents: Assisting legal professionals in understanding the essential information in lengthy and complex legal texts. 4. Customer support: Summarizing customer interactions and feedback for better understanding and decision-making. 5. Meeting and conference notes: Creating concise summaries of discussions and presentations for easy reference and knowledge sharing. As abstractive summarization technology continues to improve, it has the potential to save time and effort for professionals across various industries, enabling them to quickly grasp essential information from large volumes of text.
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