Papers with abstractive text summarization
Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network (N18-2)
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| Challenge: | Abstractive text summarization models are hard to be controlled in the process of generation, which leads to a lack of key information. |
| Approach: | They propose a guiding generation model that combines extractive and abstractive methods to generate text summarization. |
| Outcome: | The proposed model improves on the CNN/Daily Mail dataset. |
Attention Temperature Matters in Abstractive Summarization Distillation (2022.acl-long)
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| Challenge: | Recent progress of abstractive text summarization relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. |
| Approach: | They propose to distill large Transformer summarization models into smaller ones with minimal performance loss by manipulating attention temperatures in Transformers. |
| Outcome: | The proposed method outperforms vanilla pseudo-labeling based methods on three summarization datasets and is shorter and more abstractive. |
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (2021.acl-demo)
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| Challenge: | despite advances in abstractive text summarization, the true performance and failure modes of modern neural models are not yet fully understood due to the black-box nature of neural models and unmanageable scale of recent datasets for manual analysis. |
| Approach: | They propose an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of models, data, and evaluation metrics associated with text summarization. |
| Outcome: | The proposed tool can identify the shortcomings and failure modes of state-of-the-art summarization models. |
Linearizing Transformer with Key-Value Memory (2022.emnlp-main)
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| Challenge: | Efficient transformer variants with linear time complexity have been developed to mitigate the quadratic computational overhead of the vanilla transformer. |
| Approach: | They propose a linear time complexity transformer variant that reduces the quadratic computational overhead of the vanilla transformer by using a recurrent-style incremental computation similar to kernel-based transformers. |
| Outcome: | The proposed method reduces the performance gap while achieving the same efficiency even with short generation. |
Generative Bridging Network for Neural Sequence Prediction (N18-1)
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| Challenge: | Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing. |
| Approach: | They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence. |
| Outcome: | The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden. |
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net (2022.findings-naacl)
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| Challenge: | Existing methods focus on attention mechanism, but they are not suitable for abstractive text summarization. |
| Approach: | They propose a siamese generative adversarial net for abstractive text summarization which preserves the main semantics of the source text and the target summary. |
| Outcome: | The proposed model can preserve the main semantics of the source text and target summary. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
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| Challenge: | Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines. |
| Approach: | They propose a conditioned text generation model that uses a template-based approach to generate content from input text. |
| Outcome: | The proposed model outperforms baselines on abstractive text summarization and data-to-text generation. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)
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| Challenge: | Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult. |
| Approach: | They propose an unsupervised method that leverages contrastive learning to generate summaries by rewriting and paraphrasing the source documents to generate good summary. |
| Outcome: | The proposed method outperforms baseline methods on extensive experiments on source documents and fake documents. |
Active Learning for Abstractive Text Summarization (2022.findings-emnlp)
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Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov
| Challenge: | Abstractive text summarization (ATS) requires a long document and short summaries. |
| Approach: | They propose a query strategy for AL in abstractive text summarization that uses uncertainty estimation to reduce model performance. |
| Outcome: | The proposed query strategy improves ROUGE and consistency scores for annotated datasets . it also increases the performance of the model, compared to passive annotation. |
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021.findings-acl)
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Tahmid Hasan, Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | XL-Sum dataset covers 44 languages ranging from low to high-resource . Xl-SUM is highly abstractive, concise, and of high quality . |
| Approach: | They present a dataset comprising 1 million professionally annotated article-summary pairs from BBC . they fine-tune a pretrained multilingual model with XL-Sum and experiment on multilingual and lowresource tasks. |
| Outcome: | The proposed dataset is highly abstractive, concise, and of high quality . it shows higher scores on 10 languages than similar datasets compared to monolingual ones . |
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization (P19-1)
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| Challenge: | Abstractive text summarization is a demanding, time expensive and generally laborious task. |
| Approach: | They propose a framework for enhancing abstractive text summarization using deep learning techniques and semantic data transformations. |
| Outcome: | The proposed method is evaluated on two popular datasets with encouraging results. |
SumTitles: a Summarization Dataset with Low Extractiveness (2020.coling-main)
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| Challenge: | Existing methods for extractive summarization of dialogue data are limited by the grammar and structure of the utterances used. |
| Approach: | They propose a low-extractive corpus of movie dialogues for abstractive text summarization . they use an alignment algorithm to construct the corpus and a baseline evaluation . |
| Outcome: | The proposed method is low-extractive and shows high performance in dialogue datasets. |
Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization (2024.findings-emnlp)
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| Challenge: | Abstractive text summarization (ATS) requires laborious data annotation and time-consuming model training. |
| Approach: | They propose a novel active learning framework that asks large language models to rate difficulty of instances and then uses certainty gain maximization to select instances with a distribution that aligns well with the overall distribution. |
| Outcome: | The proposed framework improves stability, effectiveness, and efficiency of abstractive text summarization backbones. |
Enhancing Factual Consistency in Text Summarization via Counterfactual Debiasing (2025.coling-main)
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| Challenge: | Abstractive text summarization has produced fluent and informative outputs, but factual inconsistency is a challenge. |
| Approach: | They propose a framework that mitigates the causal effects of language bias and irrelevancy bias by counterfactual estimation. |
| Outcome: | The proposed framework outperforms baseline methods on two widely used summarization datasets. |
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining (2021.acl-long)
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| Challenge: | Abstractive text summarization has primarily focused on modeling news articles . lack of standardized datasets for summarizing online conversations is a major problem . |
| Approach: | They propose to crowdsource four new datasets for summarizing online conversations . they incorporate argument mining through graph construction to directly model issues, viewpoints, and assertions present in a conversation. |
| Outcome: | The proposed models are compared against widely-used conversation summarization datasets and show comparable or improved results. |
Creation of a Balanced State-of-the-Art Multilayer Corpus for NLU (L18-1)
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Normunds Gruzitis, Lauma Pretkalnina, Baiba Saulite, Laura Rituma, Gunta Nespore-Berzkalne, Arturs Znotins, Peteris Paikens
| Challenge: | Using full stack of language resources, we are creating a balanced text corpus for Latvian. |
| Approach: | They propose to create a syntactically and semantically annotated multilayered corpus for Latvian . they use widely acknowledged and cross-lingual representations for the corpus . |
| Outcome: | The proposed corpus adopts widely recognized and cross-lingual representations for natural language understanding and generation in Latvian. |
Stress Testing Factual Consistency Metrics for Long-Document Summarization (2026.acl-long)
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| Challenge: | Existing short-form summarization metrics struggle with input length limitations and long-range dependencies. |
| Approach: | They propose to evaluate the reliability of six widely used reference-free factuality metrics in the long-document setting by applying seven factually-preserving perturbations to summaries. |
| Outcome: | The proposed short-form summarization metrics struggle with long-range dependencies and input length limitations. |
UrduMASD: A Multimodal Abstractive Summarization Dataset for Urdu (2024.lrec-main)
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| Challenge: | a surge of multimodal content on social media has transformed our methods of communication and information exchange. |
| Approach: | They propose a video-based Urdu multimodal abstractive text summarization dataset . it uses a variety of evaluation metrics to ensure the quality of the dataset amounted to a high quality one . |
| Outcome: | The proposed dataset surpasses existing datasets on key quality metrics. |