Text summarization with ChatGPT for drug labeling documents

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

Summarizing Documents by Measuring the Importance of a Subset of Vertices within a Graph

This paper presents a novel method of generating extractive summaries for multiple documents. Given a cluster of documents, we firstly construct a graph where each vertex represents a sentence and edges are created according to the asymmetric relationship between sentences. Then we develop a method to measure the importance of a subset of vertices by adding a super-vertex into the original graph. The importance of such a super-vertex is quantified as super-centrality, a quantitative measure for the importance of a subset of vertices within the whole graph. Finally, we propose a heuristic algorithm to find the best summary. Our method is evaluated with extensive experiments. The comparative results show that the proposed method outperforms other methods on several datasets.