Complexity-Aware Scientific Literature Search: Searching for Relevant and Accessible Scientific Text

Abstract

Abstract: We conduct a series of experiments on ranking scientific abstracts in response to popular science queries issued by non-expert users. We show that standard IR ranking models optimized on topical relevance are indeed ignoring the individual user’s context and background knowledge. We also demonstrate the viability of complexity-aware retrieval models that retrieve more accessible relevant documents or ensure these are ranked prior to more advanced documents on the topic. More generally, our results help remove some of the barriers to consulting scientific literature by non-experts and hold the potential to promote science literacy in the general public. Lay Summary: In a world of misinformation and disinformation, access to objective evidence-based scientific information is crucial. The general public ignores scientific information due to its perceived complexity, resorting to shallow information on the web or in social media. We analyze the complexity of scientific texts retrieved for a lay person’s topic, and find a great variation in text complexity. A proof of concept complexity-aware search engine is able to retrieve both relevant and accessible scientific information for a layperson’s information need.

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