Personalized Abstractive Summarization by Tri-agent Generation Pipeline (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing research shows that large language models do not consistently satisfy users' preferences or expectations. |
| Approach: | They propose a tri-agent generation pipeline that includes a generator, an instructor, and an editor to enhance output personalization. |
| Outcome: | The proposed pipeline generates outputs that better meet user expectations on two abstractive summarization datasets. |
Similar Papers
Extractive Summarization via ChatGPT for Faithful Summary Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Abstractive summarization methods struggle with generating ungrammatical or even nonfactual contents. |
| Approach: | They evaluate ChatGPT's performance on extractive summarization and compare it with traditional fine-tuning methods on benchmark datasets. |
| Outcome: | The proposed pipeline performs better than abstractive methods on summary faithfulness and in-context learning. |
A New Approach to Overgenerating and Scoring Abstractive Summaries (2021.naacl-main)
Copied to clipboard
| Challenge: | Abstractive summarization is a learning objective to produce system outputs that resemble reference summaries on a word-to-word basis. |
| Approach: | They propose a two-staged strategy to generate multiple variants of the target summary and score and select admissible ones according to users’ needs. |
| Outcome: | The proposed approach can achieve state-of-the-art on benchmark summarization datasets. |
Extractive Summarization with Text Generator (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing extractive systems lack gold training signals, thereby hindering learning of extractive models. |
| Approach: | They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries. |
| Outcome: | The proposed method can be used to train extractive summarizers without training . it is shown that the approximated summaries correlate positively with the auxiliary summary outputs. |
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net (2022.findings-naacl)
Copied to clipboard
| 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. |
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models focus on local word prediction, and cannot make high level plans on what to generate. |
| Approach: | They propose a pipelined system that summarises, outlines and elaborates on each bullet point to generate the corresponding segment. |
| Outcome: | The proposed system produces long texts with significantly better quality and faster convergence speed. |
Improving Abstraction in Text Summarization (D18-1)
Copied to clipboard
| Challenge: | Abstractive text summarization models do not capture the abstractive nature of high quality summaries. |
| Approach: | They propose to decompose a decoder into a contextual network and a pretrained language model that incorporates prior knowledge about language generation. |
| Outcome: | The proposed model achieves comparable results to state-of-the-art models, based on ROUGE scores and human evaluations, while producing a significantly higher level of abstraction. |
Few-Shot Learning for Opinion Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that abstractive summarization models fail to capture their essential properties due to the high cost of summary production. |
| Approach: | They propose a few-shot framework for abstractive opinion summarization that bootstraps the output of an unsupervised model. |
| Outcome: | The proposed framework outperforms extractive and abstractive methods on Amazon and Yelp datasets. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
Copied to clipboard
| 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. |
Guided Profile Generation Improves Personalization with Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to personalization with LLMs rely on sparse and complex personal contexts, resulting in incomplete interpretation. |
| Approach: | They propose a general method to generate personal profiles in natural language that extracts important, distinctive features from the personal context into concise, descriptive sentences. |
| Outcome: | The proposed method improves personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |