ACC o RD : A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts (2022)

Abstract: Systems that automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge. However, current systems assume a single “best” description per concept, which fails to account for the many ways a concept can be described. We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts. Our system takes advantage of the myriad ways a concept is mentioned across the scientific literature to produce distinct, diverse descriptions oftarget concepts in terms of different reference concepts. In a user study, we find that users prefer (1) descriptions produced by our end-to-end system, and (2) multiple descriptions to a single “best” description. We release the ACCoRD corpus which includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions to support research on our task.

## introduction readers of scientific papers often encounter unfamiliar concepts, which impedes their understanding @xcite . this is because papers assume a priori knowledge, and often lack definitions for the scientific terms that they use. while readers may turn to external encyclopedic resources like wikipedia, these contain descriptions for only a small fraction of scientific concepts @xcite , which has motivated the development of systems that automatically extract or generate descriptions for scientific concepts. unfortunately, current systems only surface a single "best" result for all users, which is often extracted from a single input document @xcite @xcite . the one-best description may not be accessible for all readers, given varying background knowledge. ## data set to support work on dsg, we compile and release the accord corpus. the data set consists of 1,275 labeled contexts and 1,787 hand-authored concept descriptions, and induces diversity among these concept descriptions in two key ways. first, our data set allows for concept descriptions beyond the typical is-a relation. second, a single target concept is allowed to be described in terms of any number of other concepts in the source text. ## system overview the accord system has 3 pipeline stages: (1) extract sentences that describe one scientific concept in terms of another, (2) generate succinct, self-contained descriptions of the concepts' relationship, and (3) select the top descriptions for each concept (see figure 3 ). generation each extracted candidate is then input into a generative model, which produces a succinct, self-contained summary of the concept relationship described in the context. for the generator, we use gpt-3's davicini-instruct-beta model @xcite in the few-shot setting. details on how gpt-3 was prompted and heuristically post-processed are in appendix a.4. selection for each target concept, we identify a smaller, easily-consumable set foot_0 of informative descriptions from the larger pool of candidates. first, we filter descriptions to only those that involve a reference concept from forecite @xcite . second, note that each description has an associated context classified with relations in the extraction step. using these, for each (target, relation) pair, we choose the most frequent k reference concepts among the descriptions. third, we select a top description for each @xcite triple by selecting the one with the highest predic-tion score from our multilabel extraction model. ## user study the experiments in the previous section show that our system produces meaningful diversity in generated descriptions. we perform a user study with the full end-to-end system in order to answer two key questions regarding our system's utility: ## related work learning new concepts cognitive theories of learning have asserted that effective ways of describing a new concept to someone tend to take advantage of structured background knowledge @xcite by grounding descriptions to already-familiar concepts @xcite . systems that assume a single "best" result for all users limit the accessibility of technical knowledge to diverse audiences @xcite . these considerations motivate our system and novel task definition, which extends the conventional description generation setting to include multiple target descriptions for a single concept. description generation while previous work has investigated extracting and generating definitions of scientific concepts @xcite @xcite , they focus on producing a single canonical description for each concept. these methods also approach definition generation from a single-document perspective, which doesn't account for the multitude of ways a concept might be described outside of the context of that paper. in contrast, we aim to preserve multiple distinct descriptions that take advantage of the corpus-wide mentions of a given concept. in addition, unlike the data sets these models are trained on (e.g. w00 @xcite ), our accord corpus includes descriptions that involve comparative relationships between concepts beyond the typical is-a relationship. such extractions of concept comparisons have been targeted in the context of relation extraction systems @xcite , and their corresponding data sets @xcite . however, these do not include differentia between the concept pairs that elaborate on the concepts' relationships, making them unsuitable as data for our concept description setting. ## conclusion we have presented accord, an end-to-end system for the novel task of description set generation (dsg). in user studies, our methods were preferred over baseline approaches and produce a diversity of generated concept descriptions. we also release the accord corpus to facilitate development of future systems for dsg. we hope that such systems will help increase the accessibility of scientific literature for people with diverse background knowledge.