Factual Consistency Evaluation of Summarisation in the Era of Large Language Models

Factual inconsistency with source documents in automatically generated summaries can lead to misinformation or pose risks. Existing factual consistency(FC) metrics are constrained by their performance, efficiency, and explainability. Recent advances in Large language models (LLMs) have demonstrated remarkable potential in text evaluation but their effectiveness in assessing FC in summarisation remains underexplored. Prior research has mostly focused on proprietary LLMs, leaving essential factors that affect their assessment capabilities unexplored. Additionally, current FC evaluation benchmarks are restricted to news articles, casting doubt on the generality of the FC methods tested on them. In this paper, we first address the gap by introducing TreatFact a dataset of LLM-generated summaries of clinical texts, annotated for FC by domain experts. Moreover, we benchmark 11 LLMs for FC evaluation across news and clinical domains and analyse the impact of model size, prompts, pre-training and fine-tuning data. Our findings reveal that despite proprietary models prevailing on the task, open-source LLMs lag behind. Nevertheless, there is potential for enhancing the performance of open-source LLMs through increasing model size, expanding pre-training data, and developing well-curated fine-tuning data. Experiments on TreatFact suggest that both previous methods and LLM-based evaluators are unable to capture factual inconsistencies in clinical summaries, posing a new challenge for FC evaluation.

The Lay Person's Guide to Biomedicine: Orchestrating Large Language Models

Automated lay summarisation (LS) aims to simplify complex technical documents into a more accessible format to non-experts. Existing approaches using pre-trained language models, possibly augmented with external background knowledge, tend to struggle with effective simplification and explanation. Moreover, automated methods that can effectively assess the `layness' of generated summaries are lacking. Recently, large language models (LLMs) have demonstrated a remarkable capacity for text simplification, background information generation, and text evaluation. This has motivated our systematic exploration into using LLMs to generate and evaluate lay summaries of biomedical articles. We propose a novel \textit{Explain-then-Summarise} LS framework, which leverages LLMs to generate high-quality background knowledge to improve supervised LS. We also evaluate the performance of LLMs for zero-shot LS and propose two novel LLM-based LS evaluation metrics, which assess layness from multiple perspectives. Finally, we conduct a human assessment of generated lay summaries. Our experiments reveal that LLM-generated background information can support improved supervised LS. Furthermore, our novel zero-shot LS evaluation metric demonstrates a high degree of alignment with human preferences. We conclude that LLMs have an important part to play in improving both the performance and evaluation of LS methods.

The FinBen: An Holistic Financial Benchmark for Large Language Models

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of thorough evaluations and the complexity of financial tasks. This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs. In this paper, we introduce FinBen, the first comprehensive open-sourced evaluation benchmark, specifically designed to thoroughly assess the capabilities of LLMs in the financial domain. FinBen encompasses 35 datasets across 23 financial tasks, organized into three spectrums of difficulty inspired by the Cattell-Horn-Carroll theory, to evaluate LLMs' cognitive abilities in inductive reasoning, associative memory, quantitative reasoning, crystallized intelligence, and more. Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals insights into their strengths and limitations within the financial domain. The findings indicate that GPT-4 leads in quantification, extraction, numerical reasoning, and stock trading, while Gemini shines in generation and forecasting; however, both struggle with complex extraction and forecasting, showing a clear need for targeted enhancements. Instruction tuning boosts simple task performance but falls short in improving complex reasoning and forecasting abilities. FinBen seeks to continuously evaluate LLMs in finance, fostering AI development with regular updates of tasks and models.

Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles

This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this shared task is to develop abstractive summarisation models capable of generating "lay summaries" (i.e., summaries that are comprehensible to non-technical audiences) in both a controllable and non-controllable setting. There are two subtasks: 1) Lay Summarisation, where the goal is for participants to build models for lay summary generation only, given the full article text and the corresponding abstract as input; and 2) Readability-controlled Summarisation, where the goal is for participants to train models to generate both the technical abstract and the lay summary, given an article's main text as input. In addition to overall results, we report on the setup and insights from the BioLaySumm shared task, which attracted a total of 20 participating teams across both subtasks.

Graph Contrastive Topic Model

Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with similar semantics to the prototypes. In this paper, we aim to explore the efficient sampling strategy and contrastive learning in NTMs to address the aforementioned issue. We propose a new sampling assumption that negative samples should contain words that are semantically irrelevant to the prototype. Based on it, we propose the graph contrastive topic model (GCTM), which conducts graph contrastive learning (GCL) using informative positive and negative samples that are generated by the graph-based sampling strategy leveraging in-depth correlation and irrelevance among documents and words. In GCTM, we first model the input document as the document word bipartite graph (DWBG), and construct positive and negative word co-occurrence graphs (WCGs), encoded by graph neural networks, to express in-depth semantic correlation and irrelevance among words. Based on the DWBG and WCGs, we design the document-word information propagation (DWIP) process to perform the edge perturbation of DWBG, based on multi-hop correlations/irrelevance among documents and words. This yields the desired negative and positive samples, which will be utilized for GCL together with the prototypes to improve learning document topic representations and latent topics. We further show that GCL can be interpreted as the structured variational graph auto-encoder which maximizes the mutual information of latent topic representations of different perspectives on DWBG. Experiments on several benchmark datasets demonstrate the effectiveness of our method for topic coherence and document representation learning compared with existing SOTA methods.

A Survey on Biomedical Text Summarization with Pre-trained Language Model

The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), provides a big challenge for clinicians and researchers to access clinical information efficiently. To address the problem, biomedical text summarization has been proposed to support clinical information retrieval and management, aiming at generating concise summaries that distill key information from single or multiple biomedical documents. In recent years, pre-trained language models (PLMs) have been the de facto standard of various natural language processing tasks in the general domain. Most recently, PLMs have been further investigated in the biomedical field and brought new insights into the biomedical text summarization task. In this paper, we systematically summarize recent advances that explore PLMs for biomedical text summarization, to help understand recent progress, challenges, and future directions. We categorize PLMs-based approaches according to how they utilize PLMs and what PLMs they use. We then review available datasets, recent approaches and evaluation metrics of the task. We finally discuss existing challenges and promising future directions. To facilitate the research community, we line up open resources including available datasets, recent approaches, codes, evaluation metrics, and the leaderboard in a public project: https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master.

ChatGPT as a Factual Inconsistency Evaluator for Text Summarization

The performance of text summarization has been greatly boosted by pre-trained language models. A main concern of existing methods is that most generated summaries are not factually inconsistent with their source documents. To alleviate the problem, many efforts have focused on developing effective factuality evaluation metrics based on natural language inference, question answering, and syntactic dependency et al. However, these approaches are limited by either their high computational complexity or the uncertainty introduced by multi-component pipelines, resulting in only partial agreement with human judgement. Most recently, large language models(LLMs) have shown excellent performance in not only text generation but also language comprehension. In this paper, we particularly explore ChatGPT's ability to evaluate factual inconsistency under a zero-shot setting by examining it on both coarse-grained and fine-grained evaluation tasks including binary entailment inference, summary ranking, and consistency rating. Experimental results indicate that ChatGPT generally outperforms previous evaluation metrics across the three tasks, indicating its great potential for factual inconsistency evaluation. However, a closer inspection of ChatGPT's output reveals certain limitations including its preference for more lexically similar candidates, false reasoning, and inadequate understanding of instructions.

ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization

The performance of abstractive text summarization has been greatly boosted by pre-trained language models recently. The main concern of existing abstractive summarization methods is the factual inconsistency problem of their generated summary. To alleviate the problem, many efforts have focused on developing effective factuality evaluation metrics based on natural language inference and question answering et al. However, they have limitations of high computational complexity and relying on annotated data. Most recently, large language models such as ChatGPT have shown strong ability in not only natural language understanding but also natural language inference. In this paper, we study the factual inconsistency evaluation ability of ChatGPT under the zero-shot setting by evaluating it on the coarse-grained and fine-grained factuality evaluation tasks including binary natural language inference (NLI), summary ranking, and consistency rating. Experimental results show that ChatGPT outperforms previous SOTA evaluation metrics on 6/9 datasets across three tasks, demonstrating its great potential for assessing factual inconsistency in the zero-shot setting. The results also highlight the importance of prompt design and the need for future efforts to address ChatGPT's limitations on evaluation bias, wrong reasoning, and hallucination.

CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization

The citation graph is essential for generating high-quality summaries of scientific papers, in which references of a scientific paper and their correlations provide extra knowledge for understanding its background and main contributions. Despite the promising role of the citation graph, effectively incorporating it still remains a big challenge, given the difficulty of accurately identifying and leveraging relevant contents in references for a source paper, as well as modelling their correlations of different intensities. Existing methods either ignore or utilize only abstracts indiscriminately from references, failing to tackle the challenge mentioned above. To fill the gap, we propose a novel citation-aware scientific paper summarization framework based on the citation graph, with the ability to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references. Specifically, we first build a domain-specific dataset PubMedCite with about 192K biomedical scientific papers and a large citation graph preserving 917K citation relationships between them. It is characterized by preserving the salient contents extracted from full texts of references, and the weighted correlation between the salient contents of references and the source paper. Based on it, we design a self-supervised citation-aware summarization framework (CitationSum) with graph contrastive learning, which boosts the summarization generation by efficiently fusing the salient information in references with source paper contents under the guidance of their correlations. Experimental results show that our model outperforms the state-of-the-art methods, due to efficiently leveraging the information of references and citation correlations.