No perspective, no perception!! Perspective-aware Healthcare Answer Summarization (2024)

Abstract: Healthcare Community Question Answering (CQA) forums offer an accessible platform for individuals seeking information on various healthcare-related topics. People find such platforms suitable for self-disclosure, seeking medical opinions, finding simplified explanations for their medical conditions, and answering others’ questions. However, answers on these forums are typically diverse and prone to off-topic discussions. It can be challenging for readers to sift through numerous answers and extract meaningful insights, making answer summarization a crucial task for CQA forums. While several efforts have been made to summarize the community answers, most of them are limited to the open domain and overlook the different perspectives offered by these answers. To address this problem, this paper proposes a novel task of perspective-specific answer summarization. We identify various perspectives, within healthcare-related responses and frame a perspective-driven abstractive summary covering all responses. To achieve this, we annotate 3167 CQA threads with 6193 perspective-aware summaries in our PUMA dataset. Further, we propose PLASMA, a prompt-driven controllable summarization model. To encapsulate the perspective-specific conditions, we design an energy-controlled loss function for the optimization. We also leverage the prefix tuner to learn the intricacies of the healthcare perspective summarization. Our evaluation against five baselines suggests the superior performance of PLASMA by a margin of ~1.5 - 21% improvement. We supplement our experiments with ablation and qualitative analysis.

## introduction in this digital age, community question-answering (cqa) platforms like quora, reddit, and yahoo! answers have significantly transformed the way we exchange information. these platforms enable users from around the globe to share knowledge, experiences, and opinions, fostering a unique collaborative environment. among the various topics discussed on these platforms, medical advising and interactions have gained notable popularity, such as reddit's r/askdocs. users seek help by posting their questions and peers respond to them. however, the diverse nature of responses, as well as their overwhelming number, make finding reliable medical insights a challenging and non-trivial task. summarizing the responses (or answers) in a concise and meaningful way offers a tangible solution. moreover, these responses offer a wide range of perspectives such as personal experiences, factual information, advice, etc., that an end user finds relevant @xcite @xcite . for example, figure 1 depicts an instance of a cqa thread where a user seeks advice on alternatives to surgery for gallstones. in response, peers responded with varied perspectives, e.g., peer1 provided perspectives on general information, offer some suggestion, talks about personal experience and potential implications. we observe a similar trend in other responses as well, where users present perspectives on the causes of the medical problem or pose questions to better understand the context or situation presented in the main question. capturing these diverse perspectives in summaries is crucial @xcite since these varied insights are invaluable for users to make informed healthcare decisions and access appropriate support. despite the apparent need, existing research on medical summarization predominantly focuses on medical reports @xcite or medical dialogues @xcite , and overlooks the role of perspectives in crafting effective summaries. recently, @xcite identified the role of perspective-guided summarization and created a dataset with 200 cqa threads labeled with vari-question: i was just diagnosed with gallstones in my gall bladder i really don't want to have surgery and have been told that there are other ways to get rid of the stones. suggestions? ## related work recent advancements in pre-trained language models (plms) have markedly improved performance in abstractive text summarization tasks. notable examples include bart @xcite , t5 @xcite , and pegasus @xcite , which have achieved state-of-theart results particularly in summarizing news content, as demonstrated on large datasets such as cnn/dailymail @xcite and xsum @xcite ) @xcite . in the biomedical and healthcare domain, significant advancements have been made in summarizing diverse types of content, including biomedical literature @xcite , consumer healthcare questions @xcite @xcite @xcite , and medical notes @xcite . these efforts predominantly utilize pretrained language models (plms) such as biobert @xcite , biobart @xcite , and clinicalbert @xcite , which have been trained on extensive biomedical corpora like pubmed and mimic-iii. although these models demonstrate remarkable proficiency in generating fluent summaries, they often fall short in producing faithful summaries. early research in multi-document summarization (mds), like @xcite , focused on extracting key information across documents and produce a unified summary. a similar idea is underlined in @xcite , for the news domain cnn/daily mail corpus @xcite . extensive research on news articles was presented as a part of the duc foot_0 and tac foot_1 tasks. @xcite introduced a query-focused multi-perspective summarization on a qa dataset with sentence-level spans. joshi et al. (2020a) and michalopoulos et al. (2022b) do the same by exploiting local and global features of the text. ctrlsum @xcite introduces a method that allows interaction during inference without predefined aspects to guide the model. cqasumm @xcite highlighted the challenges of applying mds on high-variance, opinion-based cqa data, revealing the limitations of modelling on fact-rich data. the dataset released by @xcite is the first in the medical domain to evaluate query-focused summaries albeit using only managed sources for data. perspective-based summarization is typically a two-step process of first identifying relevant sentences, followed by summarization. in answer-summ @xcite , a model is used to extract sentences similar to the query. alternatively, spanbert @xcite , with its modified pre-training process, has shown good results in span-related tasks. in the biomedical realm, @xcite used both word-level and sentence-level attention to detect medical outcome spans. the work @xcite uses dependency trees with a gcn @xcite and a transformer to detect spans with dysfluencies. our work here is unique in these ways: ## dataset this section describes the dataset development process of puma, which is comprised of two stages of annotations: a) perspective and span identification; and b) perspective-driven summary. ## methodology in this section, we systematically outline the proposed architecture, plasma, to generate perspective-specific summaries, depicted in figure 2. given an input -a question q, a set of answers a 1 , a 2 , . . . , a n , and a desired perspective p , we design a prompt to be fed to plasma for generating the perspective-controlled summary s p . plasma incorporates flan-t5 as the foundational model clubbed with a prefix tuner that adapts the model to the intricacies of the medical perspective summarization. further, we optimize the architecture through a combination of an energy-controlled domain-specific loss function (ℓ perspective ) and a standard cross-entropy loss function (ℓ ce ). ## experiments and results we benchmark puma on multiple state-of-the-art approaches. for comparison, we compute rouge (r1, r2, and rl) @xcite , bleu @xcite , meteor @xcite , and bertscore @xcite . comparison with gpt-4. ablation study. ablation results are furnished in ## conclusion in response to the lack of perspective-specific summarization datasets in healthcare, we introduced puma a pioneering dataset specifically designed for the perspective-specific summarization task. our dataset features five perspective labels (suggestion, information, question, cause, and experience). further, to benchmark the dataset, we propose plasma which incorporates efficient customization of the model's behavior based on the input prompt without the need for extensive retraining and energy-based loss to cater to a custom-designed multi-attribute input prompt. we conducted an extensive evaluation (i.e., empirical, qualitative) to establish the effectiveness of the plasma.