Enhancing Biomedical Lay Summarisation with External Knowledge Graphs (2023)

Abstract: Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e.g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience. We address this issue by augmenting eLife, an existing biomedical lay summarisation dataset, with article-specific knowledge graphs, each containing detailed information on relevant biomedical concepts. Using both automatic and human evaluations, we systematically investigate the effectiveness of three different approaches for incorporating knowledge graphs within lay summarisation models, with each method targeting a distinct area of the encoder-decoder model architecture. Our results confirm that integrating graph-based domain knowledge can significantly benefit lay summarisation by substantially increasing the readability of generated text and improving the explanation of technical concepts.

## introduction lay summarisation consists of generating a concise summary that illustrates the significance of a longer technical (or otherwise specialist) text and is comprehensible to the non-expert @xcite . a lay summary should contain minimal jargon and technical details (e.g., methodology), instead focusing largely on the simplification of key technical concepts and the explanation or relevant background information, thus allowing readers without technical knowledge to grasp the general topic and main ideas of an article @xcite . however, since the original article is intended for a technical audience who already possess some domain knowledge, it is unlikely to explicitly include all the information necessary for the lay summary, such as background details or definitions. as a result, lay summaries are often highly abstractive, adopting a simpler lexicon than the original article @xcite , and are typically written by experts who possess the knowledge required to simplify and explain the contents of the article @xcite . despite this disparity between lay summary and source article, automatic approaches to lay summarisation have typically relied solely upon the source article as input @xcite @xcite . aiming to address this, we propose enhancing lay summarisation models with external domain knowledge, conducting the first study on knowledge graphenhanced lay summarisation with a focus on biomedical articles. we augment elife @xcite , an existing high-quality lay summarisation dataset, with article-specific knowledge graphs containing information on the technical concepts covered within the articles and the relation-ships between them (exemplified in figure 1 ), thus providing a structured representation of the domain knowledge that an expert human author might draw upon when writing a lay summary ( §3). in doing this, we hypothesise that a model's ability to simplify and explain technical concepts for a lay audience will improve. although other forms of summarisation task (e.g., news articles) have seen significant research in the enhancing models using knowledge graphs @xcite @xcite , this has yet to be explored for lay summarisation. to our knowledge, there is no work on determining the most effective way to incorporate graph-based knowledge for lay summarisation or other summarisation tasks. therefore, we systematically investigate the effectiveness of three different methods for injecting graph-based information into lay summarisation models ( §4) assessing them with both automatic and human evaluation ( §5 & §6). our results demonstrate that the integration of graph-based domain knowledge can significantly improve automatic lay summarisation enabling models to generate substantially more readable text and to better explain technical concepts. ## article knowledge graphs we augment elife, an existing dataset for biomedical lay summarisation with heterogeneous articlespecific knowledge graphs (kgs). each kg contains structured information on the complex biomedical concepts covered within the article and the relationships between them. in order to localise this information and provide an indication of where in an article a concept is mentioned, we also choose to model the article's section-based document structure within our graphs through the use of sectionspecific nodes. in the following, we describe in detail: 1) our process for extracting the knowledge that is used by our model ( §3.1), and 2) how we structure that knowledge within a knowledge graph ( §3.2). the methods by which we integrate graphbased knowledge into summarisation models are discussed in §4. ## knowledge-enhanced lay summarisation approaches we investigate the effectiveness of three different methods for incorporating external knowledge from article graphs into encoder-decoder-based summarisation models. our experiments are carefully designed so as to target a distinct aspect within the model architecture (i.e., the input, the encoder, and the decoder) with each selected method, taking inspiration from models that have recently been proven effective in the domain of news article summarisation @xcite . figure 3 provides a visualization of how each of these approaches fits into this architecture. to allow the ingestion of the full input article, we make use of longformer encoder-decoder @xcite ) as our base model for all experiments. this bart-based model replaces standard transformer self-attention with a sparse attention mechanism that scales linearly to the sequence length, enabling the processing of longer texts (such as research articles). we describe each knowledge-enhancement approach in detail below. these graph neural network (gnn) models produce a final set of node features (i.e., a graph embedding) by using attention layers to efficiently aggregate over the features of neighboring nodes, and are widely used in current literature to aggregate graph-based information for nlg tasks @xcite @xcite . during the decoding phase, we follow previous works @xcite ) by forcing our model to attend to the kg embedding h g . specifically, in every transformer layer of the decoder, we include a second cross-attention mechanism that occurs directly after the standard encoder cross-attention (see arrow a in figure 3 ) and attends to the output of the gat-based model. specifically, we concatenate the document and graph embeddings, before passing them through an additional encoder layer. for a given input document x, this process can be formalised as follows: ## conclusion this papers presents the first study on the use of knowledge graphs to enhance lay summarisation, augmenting the biomedical lay summarisation dataset elife with article-specific knowledge graphs containing domain-specific external knowledge on relevant technical concepts. we compare three distinct approaches for incorporating graphbased knowledge into encoder-decoder summarisation models, placing an emphasis on the readability and factual correctness of the generated output. our results suggest that integrating external knowledge has the potential to substantially improve lay summarisation, particularly for the generation of readable text and explanation of technical concepts. we would like to see future work investigate the use of additional graph representations, as well as their integration into larger models that adopt different architectures (e.g., decoder-only).