ATLAS : Improving Lay Summarisation with Attribute-based Control (2024)

Abstract: Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences. However, previous work assumes a one-size-fits-all approach, where the content and style of the produced summary are entirely dependent on the data used to train the model. In practice, audiences with different levels of expertise will have specific needs, impacting what content should appear in a lay summary and how it should be presented. Aiming to address this, we propose ATLAS, a novel abstractive summarisation approach that can control various properties that contribute to the overall “layness” of the generated summary using targeted control attributes. We evaluate ATLAS on a combination of biomedical lay summarisation datasets, where it outperforms state-of-the-art baselines using mainstream summarisation metrics.Additional analyses provided on the discriminatory power and emergent influence of our selected controllable attributes further attest to the effectiveness of our approach.

## introduction lay summarisation is defined as producing a summary of a scientific article that is comprehensible to non-experts @xcite . recent work has shown that, when compared to technical abstracts, lay summaries typically are more readable (lexically and syntactically), more abstractive, and contain more background information, enabling a non-technical reader to better understand their contents @xcite @xcite . however, the extent to which these attributes are required within a lay summary depends largely on the specific needs of the reader. for example, a scientist from a related field will require less background information to understand an article's contents than an entirely non-technical * corresponding author reader, but they might still require domain-specific jargon to be simplified or explained. despite its obvious benefits, to our knowledge, no work has yet explored how we can enable such fine-grained control over comprehensibility-related aspects for lay summary generation. in this paper, we propose atlas (attributecontrolled lay summarization), a novel scientific summarisation approach that aims to control four attributes targeting distinct properties contributing to the overall "layness" of the generated summary, thus allowing it to cater to the specific needs of different audiences. although recent attempts at text simplification and story generation have had success influencing the style @xcite @xcite and content @xcite of generated text using fine-grained controllable attributes, no work to our knowledge has explored this for scientific summarisation. @xcite recently addressed the task of readability-controlled scientific summarisation, however, this is only done at a binary level, training a model to produce either a technical or non-technical summary based on a single control token. our approach innovates by enabling a greater degree of controllability through the flexible handling of multiple attributes, allowing it to produce more diverse summaries and better address the specific needs of different audiences. our results show that atlas outperforms state-of-the-art baselines in both automatic and human evaluations across three summary types with varying levels of technicality. additional analyses confirm that attribute control positively influences performance, and suggest the selected control attributes are able to effectively capture the difference between technical and nontechnical summaries. ## methodology as discussed in §1, atlas aims to control four targeted attributes. we use bart-base as the base model for atlas as it represents the state-of-theart benchmark in previous lay summarisation works @xcite . formally, each document @xmath0, x 2 , ..., x n ) of length n, where x i is the i-th token, is prepended with a control token sequence l such that @xmath1, x 1 , x 2 s, ..., x n ). l consists of our four selected control tokens, each of which targets distinct characteristics of the output summary that contributes to its overall comprehensibility. we describe each aspect below: length (l) the length of the output summary in characters. a more lay audience may require a longer summary to aid comprehension. readability (r) how easy it is to read the text. this is measured using the flesh-kincaid grade level (fkgl) metric, which estimates the reading grade level (us) required to understand the generated text based on the total number of sentences, words, and syllables present within it. ## conclusion in this paper, we introduce atlas, a model for controllable lay summarisation that employs controllable attribute tokens to influence various properties of the generated summary, enabling it to cater to users of different levels of expertise. using combined datasets for biomedical lay summarisation we perform multiple experiments whereby we confirm the ability of our selected control attributes to discriminate between summary types, demonstrate their effectiveness for controllable lay summarisation, and further investigate their ability to effectuate desired differences during generation.