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.

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

The current advancement in abstractive document summarization depends to a large extent on a considerable amount of human-annotated datasets. However, the creation of large-scale datasets is often not feasible in closed domains, such as medical and healthcare domains, where human annotation requires domain expertise.
This paper presents a novel data selection strategy to generate diverse and semantic questions in a low-resource setting with the aim to summarize healthcare questions. Our method exploits the concept of guided semantic-overlap and diversity-based objective functions to optimally select the informative and diverse set of synthetic samples for data augmentation. Our extensive experiments on benchmark healthcare question summarization datasets demonstrate the effectiveness of our proposed data selection strategy by achieving new state-of-the-art results. Our human evaluation shows that our method generates diverse, fluent, and informative summarized questions.

Towards Summarizing Healthcare Questions in Low-Resource Setting

Health, demographic change and wellbeing

The growth of online consumer health questions has led to the necessity for reliable and
accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization
of long questions is a challenging task due to
the lack of training data and the complexity of
the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization.
We propose two novel rewards obtained from
the downstream tasks of (i) question-type identification and (ii) question-focus recognition
to regularize the question generation model.
These rewards ensure the generation of semantically valid questions and encourage the
inclusion of key medical entities/foci in the
question summary. We evaluated our proposed method on two benchmark datasets and
achieved higher performance over state-of-the-art models. The manual evaluation of the
summaries reveals that the generated questions are more diverse and have fewer factual
inconsistencies than the baseline summaries.
The source code is available here: https://github.com/shwetanlp/CHQ-Summ.

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

The unprecedented growth of Internet users has
resulted in an abundance of unstructured information on social
media including health forums, where patients request health-related information or opinions from other users. Previous studies
have shown that online peer support has limited effectiveness
without expert intervention. Therefore, a system capable of assessing the severity of the health state from the patients’ social media
posts can help health professionals (HP) in prioritizing the users
post. In this study, we inspect the efficacy of different aspects of
Natural Language Understanding (NLU) to identify the severity
of the users health state in relation to two perspectives(tasks) (a)
Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and
(b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect,
Other) in online health communities. We propose a multiview
learning framework that models both the textual content as well
as contextual-information to assess the severity of the users health
state. Specifically, our model utilizes the NLU views such as
sentiment, emotions, personality, and use of figurative language
to extract the contextual information. The diverse NLU views
demonstrate its effectiveness on both the tasks and as well as on
the individual disease to assess a users health.

Assessing the Severity of Health States based on Social Media Posts

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. 
The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT layers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can
demonstrably improve the model’s robustness and reliability for distinguishing the depression symptoms.

Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the context beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. We evaluate our framework on the benchmark MEDIQA-RQE dataset and manifest that the use of knowledge enriched dual-encoding mechanism help in achieving an absolute improvement of 8.27% over SOTA language models.

Medical Knowledge-enriched Textual Entailment Framework

Findings In-Person Poster Session 4

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Grand Challenge D (virtual) Health, demographic change and wellbeing

technical paper

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Poster 1R: Summarization

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IJCNLP-AACL 2021

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PS T1.11: AI, Generative and Graph-based Networks

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
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The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

POSTER3: Applications:  Biomedical, health records and medical texts

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Shweta Yadav

7

22

SHORT BIO

Presentations

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

Towards Summarizing Healthcare Questions in Low-Resource Setting

Health, demographic change and wellbeing

Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

Assessing the Severity of Health States based on Social Media Posts

Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

Medical Knowledge-enriched Textual Entailment Framework

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