FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing summarization models produce unfaithful outputs for medical text summarizing . a framework to improve faithfulness is proposed to improve medical text summary accuracy . |
| Approach: | They propose a framework to improve faithfulness by fine-tuning pre-trained language models based on medical knowledge. |
| Outcome: | The proposed framework improves faithfulness on medical summarization tasks. |
Similar Papers
STORYSUMM: Evaluating Faithfulness in Story Summarization (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for evaluating abstractive summarization are lacking in faithfulness evaluation. |
| Approach: | They propose a dataset that measures faithfulness of LLM summaries with localized errors and faithfulness labels for evaluation methods. |
| Outcome: | The proposed method does not achieve more than 70% accuracy on this task. |
Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical Summaries (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Generating plain biomedical summaries with Large Language Models (LLMs) can enhance access to biomedically knowledge. |
| Approach: | They propose a benchmark dataset with expert-annotated Faithfulness and Reasoning on plain biomedical summaries. |
| Outcome: | The proposed dataset shows that LLMs perform poorly in generating faithful biomedical summaries and that abstractiveness and faithfulness are negatively correlated. |
Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success) (2023.acl-short)
Copied to clipboard
| Challenge: | Large language models are capable of producing high quality summaries of general domain news articles in few- and zero-shot settings, but it is unclear whether they are similarly capable in more specialized domains such as biomedicine. |
| Approach: | They use GPT-3 to generate single- and multi-document summaries of biomedical articles, given no supervision, using a set of annotations. |
| Outcome: | The proposed model outperforms fully supervised models in generic news summarization, but struggles to synthesize evidence across multiple documents. |
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2020.acl-main)
Copied to clipboard
| Challenge: | Existing abstractive summarization models do not guarantee factual correctness of summaries . |
| Approach: | They propose a framework where models evaluate factual correctness by fact-checking it against its reference using an information extraction module. |
| Outcome: | The proposed method significantly improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones. |
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for generating abstractive summarization are inconsistent and rely on heuristically created data for error handling. |
| Approach: | They propose a contrastive learning formulation that leverages both positive and negative summaries to train summarization systems that are better at distinguishing between them. |
| Outcome: | The proposed learning framework produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training. |
Analyzing and Evaluating Faithfulness in Dialogue Summarization (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on faithfulness of text summarization have not been conducted on abstractive summarizing. |
| Approach: | They propose a method to evaluate faithfulness of dialogue summarization models by multi-choice questions. |
| Outcome: | The proposed method can facilitate the development of dialogue summarization systems. |
SumPubMed: Summarization Dataset of PubMed Scientific Articles (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing summarization models that can extract the top few lines of news articles fail to summarize long documents. |
| Approach: | They constructed a scientific summarization dataset from MEDLINE articles from the PubMed archive to address this problem. |
| Outcome: | The proposed model outperforms existing models on news article summarization datasets and shows that it is more efficient to extract the top few lines. |
On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
| Approach: | They propose to use pretrained models for document summarization to better understand hallucinations . they find that textual entailment measures better correlate with faithfulness . |
| Outcome: | The proposed models generate faithful and factual summaries as evaluated by humans. |
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to evaluate summary faithfulness are sub-optimal due to the granularity level considered for premises and hypotheses. |
| Approach: | They propose a novel approach that uses a variable premise size and simplifies summary sentences into shorter hypotheses. |
| Outcome: | The proposed model performs better on diverse summarisation tasks than existing models. |
Learning to Revise References for Faithful Summarization (2022.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that noisy reference summaries can be detrimental to model performance. |
| Approach: | They propose to selectively re-write unsupported reference sentences to better reflect source data. |
| Outcome: | The proposed method improves reference quality while retaining all data. |