| Challenge: | Existing text summarization systems generate summaries in a single step, but are often inadequate due to the issue of hallucination and the lack of accuracy. |
| Approach: | They propose an iterative text summarization framework based on large language models like ChatGPT that refines the generated summary iterativly through self-evaluation and feedback. |
| Outcome: | The proposed framework refines the generated summary iteratively through self-evaluation and feedback, closely resembling the iteration humans undertake when drafting and revising summaries. |
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. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
| Outcome: | Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models. |
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer (2023.acl-srw)
Copied to clipboard
| Challenge: | Large-scale language models, such as ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. |
| Approach: | They conduct a systematic inspection of ChatGPT’s performance in two controllable generation tasks and evaluate the faithfulness of the generated text. |
| Outcome: | The proposed model can adapt output to different target audiences and writing styles, and can generate coherent text with human-authored texts. |
SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2022.acl-long)
Copied to clipboard
Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang
| Challenge: | Existing methods to handle long text are limited due to time and memory complexity and limited input lengths. |
| Approach: | They propose a multi-stage split-then-summarize framework for long input summarization . their framework can process input text of arbitrary length by adjusting the number of stages . |
| Outcome: | The proposed framework outperforms existing methods on three long meeting summarization datasets and on a long document summarizing dataset. |
Summarization of Dialogues and Conversations At Scale (2023.eacl-tutorials)
Copied to clipboard
| Challenge: | Conversations are the natural communication format for people. |
| Approach: | This tutorial will survey the cutting-edge methods for summarizing written and spoken conversation. |
| Outcome: | This tutorial will examine the cutting-edge methods for summarizing written and spoken conversations, covering key sub-areas whose combination is needed for a successful solution. |
SummHelper: Collaborative Human-Computer Summarization (2023.emnlp-demo)
Copied to clipboard
| Challenge: | Existing approaches for text summarization are mostly automated, with limited space for human intervention and control. |
| Approach: | They propose a 2-phase summarization assistant that facilitates human-machine collaboration . it suggests possible content and generates a coherent summary from these selections . authors hope to improve the efficiency of the computer and human-involved approach . |
| Outcome: | The proposed summarization assistant is a 2-phase summarizing assistant . it suggests potential content and consolidates the output with visual mappings . the proposed system is available for free on youtube . |
Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models have shown remarkable capability in many downstream tasks, yet their ability to understand discourse structures of dialogues remains less explored. |
| Approach: | They aim to systematically inspect ChatGPT’s performance in two discourse analysis tasks: topic segmentation and discourse parsing. |
| Outcome: | The proposed model can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures. |
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. |
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization (P19-1)
Copied to clipboard
| 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. |
RISE: Leveraging Retrieval Techniques for Summarization Evaluation (2023.findings-acl)
Copied to clipboard
| Challenge: | Summarization evaluation approaches have relied on ROUGE for summarization, but they fall short of human evaluations. |
| Approach: | They propose a new approach to evaluate summaries by leveraging retrieval techniques . they use a dual-encoder retrieval setup to train a retrieval task . |
| Outcome: | The proposed method outperforms existing methods on two document summarization benchmarks and a long document summmarization test. |