On The Persona-based Summarization of Domain-Specific Documents (2024)

Abstract: In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large information repositories. However, every persona of a domain has different requirements of information and hence their summarization. For example, in the healthcare domain, a persona-based (such as Doctor, Nurse, Patient etc.) approach is imperative to deliver targeted medical information efficiently. Persona-based summarization of domain-specific information by humans is a high cognitive load task and is generally not preferred. The summaries generated by two different humans have high variability and do not scale in cost and subject matter expertise as domains and personas grow. Further, AI-generated summaries using generic Large Language Models (LLMs) may not necessarily offer satisfactory accuracy for different domains unless they have been specifically trained on domain-specific data and can also be very expensive to use in day-to-day operations. Our contribution in this paper is two-fold: 1) We present an approach to efficiently fine-tune a domain-specific small foundation LLM using a healthcare corpus and also show that we can effectively evaluate the summarization quality using AI-based critiquing. 2) We further show that AI-based critiquing has good concordance with Human-based critiquing of the summaries. Hence, such AI-based pipelines to generate domain-specific persona-based summaries can be easily scaled to other domains such as legal, enterprise documents, education etc. in a very efficient and cost-effective manner.

heading: 1 Introduction
text: In the rapidly expanding digital world, the exponential growth of domain-specific knowledge has posed unprecedented challenges in efficiently storing and consuming vast information repositories.
* Authors contributed equally
With the increasing complexity of managing such information, the need for generation of precise and specific summaries becomes important. This need becomes particularly evident for domain-specific data as there exist different personas within a domain who have different information requirements which should be reflected in generated summaries. For instance, if we consider the healthcare domain, there exist diverse personas1 ranging from healthcare professionals like doctors and nurses to patients who require targeted information customized to their specific roles and comprehension levels.
Traditional generic approaches to summarization have often relied on humans to perform this high cognitive load task. However, as the volume and diversity of information burgeon with growing number of domains and personas, human generated persona-based summaries encounter limitations in scalability, cost-effectiveness, and consistency. The inherent subjectivity and variability among different human summarizers hinder the reliability and efficiency of such an approach. Although there have been several approaches in prior literature with focus on generic summaries through extractive and abstractive methods (Paulus et al., 2017; Erkan and Radev, 2004) as well as goal-oriented summaries (Hayashi et al., 2021; Zhu et al., 2022) but none of them have focused on persona-based summarization of domain-specific information. Goldsack et al. (2023); Luo et al. (2022) focus on building layman-summarization comprehensible to nontechnical audiences but do not differentiate the various technical summaries based on persona and they also do not use LLMs as an alternative evaluator. Our work differs in the sense that we develop a pipelined approach that generates persona-specific training summaries (doctor, patient, normal person), fine-tune small-size LLMs on this data, and use GPT-4 to efficiently evaluate summary quality.

heading: 1 http://tiny.cc/x1guwz
text: 14291
One possible solution is to harness the power of generic large language models (LLMs) such as GPT-4 to automate the generation of persona-based summaries as such models have been used to generate data for other NLP tasks (Sun et al., 2023; Yu et al., 2023). ChatGPT 2 is also used in educational data generation (Kieser et al., 2023; Maddigan and Susnjak, 2023). However, AI-generated summaries using generic LLMs may not be guaranteed to achieve optimal accuracy across different domains unless they are trained on domain-specific data and they can also be very expensive to use for daily repeated inferences. In this paper, we take a step towards introducing a two-fold contribution aimed at overcoming these challenges of generating domain-specific persona-based summaries. Firstly, we present an efficient approach towards the training of domain-specific, small-sized Large Language Models (LLMs) on a corpus related to healthcare domain. Though data distillation from a stronger model for supervised fine-tuning is a standard method, the novelty of our work lies in the fact that we effectively employ the approach in this context to build a cost-optimised summarization framework catering to different healthcare persona given the scarcity of domain-specific data. This approach addresses the limitations of generic LLMs by aligning the trained model specifically to the intricacies of summaries in the healthcare domain. Moreover, we showcase the effectiveness of utilizing AI-based critiquing for the evaluation of summarization quality, providing a more automated and scalable solution.
Secondly, we demonstrate the strong agreement between AI-based and human-based critiquing of generated summaries, establishing the reliability of our proposed approach. This not only validates the effectiveness of domain-specific small LLMbased models in generating accurate summaries but also opens up avenues for scalability across diverse domains. The implications of our findings extend beyond healthcare, as the proposed AI pipeline can be seamlessly adapted to other domains, including legal, corporate documents, education, and more, offering a versatile and cost-effective solution for generating persona-based summaries.

heading: 2 Proposed Framework
text: We describe here our framework for training our small-size domain-specific LLM, generation of
2 https://chat.openai.com/
data for finetuning and evaluation, and other model baselines that we compare our model against.

heading: 2.1 Dataset
text: We create persona-based dataset (named ‘PersonaData’) utilizing GPT-43 with specific prompts on 1455 articles from the publicly available WebMD4 website. The mean ratio between summary length and document length is 0.2 : 1. After data generation using GPT-4, we did a step by step validation of the generated summaries using an automated approach followed by manual verification to filter out the bad generations. Around 4.89% of documentsummary pairs were filtered out. We provide the detailed filtering steps with criteria and removal pairs (in %) as shown in Table 1.
These articles, related to healthcare, form the basis for creation of their summaries related to three distinct persona: (a) Doctor: Summaries focus on medical terminology, guidelines and provide detailed technical information suitable for medical professionals. (b) Patient: Summaries are easily understandable, addressing patient concerns without excessive technical jargon, focusing on toplevel information. (c) Normal Person: Summaries are tailored for a general audience without medical background, presented in simple language and engaging for laypersons while avoiding technical terms. The dataset comprises 1091, 73 and 291 articles for training, validation, and testing respectively. Additionally, we select 50 WebMD articles and generate manual summaries for three personas (termed as Annotated-Data) using the Prolific5 annotation platform and Doctors to evaluate the GPT-4 generated summaries against human curated summaries6 (Details are in Appendix F).

heading: 2.2 Model Architecture
text: Our training process consists of employing small foundation LLMs such as Llama2 and finetuning such models on the training set of the WebMD data
3 https://openai.com/gpt-4 4 https://www.webmd.com/ 5 https://www.prolific.com/ 6 Code/Dataset details are in https://github.com/ankan2/ persona-healthcare
described above. Specifically, we use supervised fine-tuning approach (Li et al., 2023) on the pretrained vanilla model versions of Llama2. Llama27: We perform supervised fine-tuning (SFT) on the pretrained vanilla Llama2-7b and Llama2-13b models using the training data that consists of prompt-completion pairs where the prompt comprises a WebMD article in the training set and the instruction to generate a summary based on a persona (doctor/patient/normal person) and the completion is the corresponding persona-based summary generated using GPT-4. We use a parameter efficient finetuning approach i.e. Quantized Low-Rank Adaptation (QLoRA) (Dettmers et al., 2023) to optimize the training process. After training, the finetuned Llama2-7b and Llama2-13b models (referred to as L-F-7b and L-F-13b respectively) acquire the ability to generate a persona-based summary for a given medical article depending on the persona specified in the prompt. Baselines: For comparison with our finetuned models on the persona-based summary generation task, we use different state-of-the-art models as baselines such as Falcon 7b-instruction tuned model (Penedo et al., 2023), BART-large (Lewis et al., 2019), instruction-tuned Pegasus (Zhang et al., 2020) and Longformer (Beltagy et al., 2020) Base (LED-B), Large (LED-L)8. Besides these, we also use finetuned versions of T5-Large (Raffel et al., 2020) (T5-FT) and Flan-T5-Large (Chung et al., 2022)
7 https://ai.meta.com/llama/ 8 https://huggingface.co/allenai/led-base-16384
(FT5-FT) on our training data as baselines. Further, we also compare the performance with the different vanilla Llama2 model variants (7b, 13b, 70b referred to as L-V-7b, L-V-13b and L-V-70b respectively).

heading: 3 Evaluation and Results
text: We evaluate the performance of our finetuned models (L-F-7b and L-F-13b) in terms of generating high quality persona-based summaries for medical articles and compare against the baseline models. Evaluation metrics: Our evaluation relies on two different approaches: (i) Traditional - Here we use traditional metrics such as Rouge [1, 2 and L] (Lin, 2004), Meteor (Banerjee and Lavie, 2005), Bleu (Papineni et al., 2002), BERTScore (Zhang et al., 2019) [Precision (BERT-Prec), Recall (BERT-Rec) and F1score (BERT-F1)] to assess the quality of generated summaries. (ii) GPT-4 critique - Here we use the GPT-4 LLM as a critic to evaluate the quality of the model generated summaries against the gold standard GPT4 generated summary (Section 2) from different dimensions. Specifically, we provide suitable critique based prompts to GPT-4 where we evaluate the summaries based on a set of five predefined criterias (termed as GPT-4 criteria) defined below: Criteria 1: Relevance (Rel): The extent to which the generated persona-based summary is relevant to the intended persona (doctor/patient/normal person) given the document. Criteria 2: Coverage (Cov): The extent to which
the generated persona-based summary correctly covers important key points described in the gold standard persona-based summary of the document. Criteria 3: Impurity (Imp): The extent to which the persona-based summary does not contain information specific to all other possible personas {persona_set - persona}. Criteria 4: Quality (Qlt): The extent to which the persona-based summary is of overall good quality from the perspective of the intended persona. Criteria 5: Goodness (Gds): Extending from 4, we manually verify the goodness of the summary. (Details of these criteria with prompts are provided in Appendix D). Results: We provide a comparison of our finetuned models against the baselines in terms of both traditional metrics as well as GPT-4 criteria in Tables 2 and 4 respectively (all values are in %) on the WebMD test set of size 873 (prompt specific to three different persona each for 291 articles). Table 2 infers that both our finetuned models (L-F-7b and L-F-13b) achieve superior performance compared to the baseline methods in terms of traditional metrics. In fact, finetuned Llama2-13b (L-F-13b) outperforms the baselines in terms of all the traditional metrics, demonstrating the superiority of our finetuning approach which helps to adapt the model to the healthcare domain and perform better on specific applications such as persona-based summarization. Similar observation holds true when we compare the values of the GPT-4 critique based criteria shown in Table 4. Here we also compare the quality of the gold standard GPT-4 generated summaries in terms of the GPT-4 critique based criteria. We find that the finetuned Llama2-13b model (L-F-13b) can generate summaries pretty close in quality to the gold standard, while being much faster in terms of training and inference time as well as cost-effective and cheaper in terms of memory requirement. Framework: We use 80GB A100 GPU, 210MHz clock cycle along with NLTK/SpaCy python packages for all experiments. For 6 epochs, Llama2-13b takes 20 hrs for finetuning and 3 hrs for inference, Llama2-7b takes 8 hrs for finetuning and 2.5 hrs for inference (Details in Appendix E.3). Ablation study: Here we investigate the quality of persona-based summaries generated by different variations of our best performing finetuned Llama213b (L-F-13b) model on WebMD test set: (A) Performance specific to different persona: Table 3 and 5 shows the performance in terms of
standard evaluation metrics and outcomes of GPT-4 critique based criteria for each of the three persona [Doctor, Patient and Normal Person (Nor-Per)] for the best performing Llama2-13b Finetuned model (L-F-13b). We observe that the model performs uniformly across the three persona which confirms that our finetuned model generalizes well across multiple persona, generating distinct persona-based summaries for the same medical article.
(B) Validation of GPT-4 generated gold standard summaries: To verify the robustness of the GPT-4 generated summaries for the WebMD articles and to mitigate the GPT-4 introduced inherent bias in generated summaries, we perform different types of human annotation experiments:
(i) Persona-based Summary of GPT-4: We randomly select 50 different WebMD articles and provide three different persona-based (doctor, patient, normal person) summaries (without their actual labels) to three different doctors with domain knowledge expertise along with a good working proficiency in English and ask them to identify the intended persona, i.e. - which summary belongs to which specific persona. Initial human labeling is done by two doctors and any annotation discrepancy is checked and resolved by the third doctor after discussing with others. The inter-annotator agreement is found to be 0.91. On comparing with actual persona labels, we found that human labels have 86.67% accuracy for correctly identifying the actual persona which shows the reliability of GPT-4
generated persona-based summaries. (ii) Content Quality Check: We ask human annotators (doctors) to annotate summaries on the basis of whether the persona-based summary is relevant while correctly covering appropriate key points based on information need of different persona and its overall usefulness. 96% of the GPT4 generated summaries for different personas are found to be useful by human annotators (doctors).
(iii) GPT-4 Generated and Ground Truth Summary Check: Both doctors with domain expertise and GPT-4 evaluate 50 document-summary pairs in terms of whether the persona-based summary is relevant and correctly covers appropriate key points based on information need of different persona, each for GPT-4 generated summaries and ground truth summaries generated by annotators. We obtain an inter-annotator agreement of 0.893 which signifies strong consensus between human and GPT-4 based evaluation. We separately test our best fine-tuned (Llama2-13b-FT) model on the human-generated summaries (50 articles) and obtain the following scores for different metrics - Traditional: R1-52.9, R2-24.1, RL-33.2, Meteor-38.7, Bleu-18.0, Bert-P-87.7, Bert-R-84.9, Bert-F1-86.3; GPT-4 criteria: Rel-91.2, Cov-90.8, Imp-90.4, Qlt88.7, Gds-98.5. This shows that there is strong alignment between results of on human generated and GPT-4 generated summaries, signifying the high quality of GPT-4 generated summaries. (C) Validation of finetuned model generated summaries: To further investigate the reliability of our finetuned model generated summaries, we choose 50 different WebMD articles and provide persona-based summaries for each persona (generated by GPT-4 in ground truth v/s Llama2-13b finetuned model generated) to two doctors to annotate: (i) whether finetuned generated summary is better, (ii) Both are Good, (iii) Ground Truth/GPT4 summary is better and (iv) Both are bad. We find that for - 20% cases Llama2-13b finetuned model summaries are better (i), for 50% cases both finetuned and ground truth generated summaries are good (ii) and rest 30% cases ground truth generated summaries are better (iii) and no instance is found where both performs bad (iv). (D) Different LLM Evaluators: We evaluate the fine-tuned model generated summaries in the test set with Gemini model9 keeping the same prompts and criteria as used earlier for GPT-4 and the obtain
9 https://gemini.google.com/
values of the same LLM based criteria are: Rel - 95.2, Cov - 92.4, Imp - 87.6, Qlt - 90.7, Gds - 99.4. Thus, Gemini scores are also aligned with GPT-4 scores with a correlation coefficient of 0.808 (Gemini provides higher scores for all criteria except Criteria 3 - ‘Imp’). This verifies that the GPT-4 based evaluation is impartial and robust.
(E) Llama2-13b performance on Other data: We test our best performing Llama2-13b finetuned model on healthcare domain articles of OASUM (Yang et al., 2022) dataset which is publicly available. We select OASUM articles with aspects related to healthcare [Death, Diagnosis, Differential Diagnosis and Diagnosis Classification] and obtain 234 such documents. We perform GPTcritique based evaluation and observe that 82.77% of the summaries are labeled as good which signifies the robustness of our model in terms of generating high quality summaries.

heading: 4 Conclusion
text: In this paper, we propose a framework for the efficient training of a small foundation LLM on AIgenerated datasets to obtain high quality domainspecific persona-based summaries. Our focus is on training a finetuned version of Llama2 on a corpus related to healthcare domain such that the trained model captures the intricacies of personabased summaries in healthcare domain. We also demonstrate the effectiveness of using AI-based critiquing for the evaluation of the model generated summaries, providing a more automated and scalable solution. Our experiments also reveal the superior quality of persona-based summaries generated by our finetuned model compared to contemporary baselines. Further, AI-based critiquing of the summaries show high inter-annotator agreement with human-based critiquing methods, further confirming the effectiveness of our proposed approach. We plan to extend our work in generating accurate persona-based summaries for documents in other domains such as legal, enterprises, education and more, which is the focus of our future work.

heading: Acknowledgements
text: The work was supported in part by a research grant from IIT KHARAGPUR AI4ICPS I HUB FOUNDATION.

heading: Limitation and Discussion
text: There are a few limitations in our works - (i) Not all LLMs are useful (similar to GPT-4) to generate personalized contents properly - like GPT2, GPT3.5, Llama2-vanilla models do not perform very well mostly due to hallucination and not covering important informations. (ii) We only explore the data from healthcare domain, but we plan to extend our work to other domains such as legal, corporate and education among others. (iii) Our experimental dataset is only English in heathcare domain, we wish to extend the work in multilingual setup, specifically for low-resource settings in diverse domains. (iv) Prompt command is very important. Unless, we specifically mention with explanation in Prompt about different Persona (Doctor/Patient/Normal Person), GPT-4 does not perform well to generate appropriate summary.

heading: Ethical Concerns
text: We use the publicly available content of the WebMD platform for non-commercial and academic purpose only without violating any ethical concerns. The dataset neither reveals any personal sensitive information of the patients nor any toxic statement. Consent has been taken from all annotators including doctors. For experiments, we use publicly available free frameworks - Llama2, Falcon, BART, Pegasus, T5-FT, Flan-T5 (FT5-FT), LED-Base, LED-Large, Llama2-7b, 13b and 70b - vanilla and finetune.

heading: Appendix

heading: A Related Work
text: In this section, we conduct a survey of state-of-theart literature on closely related topics.
Summarization: We perform a survey of various summarization techniques, that encompasses generic approaches such as abstractive (Chopra et al., 2016; See et al., 2017; Paulus et al., 2017) and extractive summarization like TextRank (Mihalcea and Tarau, 2004) and LexRank (Erkan and Radev, 2004). Subsequent research has explored summarization in specific directions, including aspectbased summaries (Hayashi et al., 2021; Coavoux et al., 2019; Mukherjee et al., 2020; Akhtar et al., 2017) and query-focused summaries (Vig et al., 2021; Zhu et al., 2022; Xu and Lapata, 2020), but none focuses on domain-specific persona-based summarization.
Persona Concept: (Goldsack et al., 2023; Luo et al., 2022) focus on building lay-summarization for comprehensible to non-technical audiences but do not have the differentiating factor of persona concept to distinguish the various technical summaries. based on persona and no usage of LLMs as an alternative metrics to evaluate the summaries. Our work differs from the fact that we show a pipelined approach - data generation, personaspecific (doctor vs patient vs normal person) summarization with key points and GPT-4 based evaluation to save time and assist different persona to augment their knowledge on it to make conclusions more efficiently. The concept of Persona also different from the notion of intent (Mullick et al., 2022d, 2023, 2022b; Mullick, 2023b,a; Mullick et al., 2022a), entity-relation (Mullick et al., 2024; Guha et al., 2021; Mullick et al., 2022c) or opinion/fact (Mullick et al., 2017b, 2016, 2018a,b, 2019, 2017a) idea. Our work differs from the fact that we show a pipelined approach - data generation, ‘persona concept’ specific (doctor vs patient vs normal person) summarization with key points and GPT-4 based evaluation to save time and assist different persona to augment their knowledge on it to make conclusions more efficiently.
Data Generation using LLMs: Large Language Models (LLMs), such as the GPT family, have been utilized to generate training datasets for NLP tasks, addressing data scarcity issues in a cost effective manner (Yu et al., 2023; Meng et al., 2022;
Ye et al., 2022). ChatGPT 10 aids in educational data augmentation and data visualization (Kieser et al., 2023; Maddigan and Susnjak, 2023); however, in healthcare domain, GPT-3 and GPT-4 are employed for generating medical dialogue summarization data (Chintagunta et al., 2021) and radiology reports (Sun et al., 2023). However, no prior work focuses on benchmark domain-specific persona-based summary data generation with appropriate human validation. LLMs as Evaluation Metrics: The rise of LLMs presents a potential, cost-effective alternative for evaluating various NLP tasks. Existing efforts include a taxonomy of LLM-based NLG evaluation methods (Gao et al., 2024), the development of ‘ChatEval’ for assessing response quality (Chan et al., 2023), and proposed guidelines for LLMbased evaluation (Zhou et al., 2023). While some studies explore LLM-based assessments with human alignment (Liu et al., 2023), there is a lack of work utilizing LLMs to evaluate solution architectures comprehensively with a critique scoring system.
Our paper takes a significant step towards addressing the shortcomings of prior literature in terms of persona-based summary data generation followed by human valiadation as well as performing LLM based critic evaluation.

heading: B Dataset Requirement
text: Access to robust, comprehensive datasets is crucial for training NLP models to understand and generate persona-specific content, emphasizing the challenges of data scarcity and summary-making capabilities. Manual annotation of large healthcare datasets is both costly and time-intensive, demanding domain expertise and meticulous attention to detail. Consider the example where n individuals generate summaries for m persona, resulting in n × m distinct summary generations for a single article — it is a highly expensive and time consuming process. Moreover, acquiring suitable human resources for labeling is challenging, as people are often hesitant to undertake the tedious and difficult task of summary generation, even with a standard payment agreement.
We provide healthcare data to the Prolific annotation platform with specific criteria: annotators
10https://chat.openai.com/
with a PhD / Graduate degree, a medical background, approval rates of 90%-100%, and expertise in medicine. However, out of 157,341 potential annotators, only 189 meet these criteria, and even among them, there is a high rejection rate of 71.43% for selecting documents for manual summary generation, despite offering more than standard payment. Further, eligible annotators tend to heavily rely on ChatGPT and similar automated approaches, leading to the need for extensive reevaluations and revisions, along with issues related to annotator rejection, even to assess a limited number of documents. Hence, obtaining a high quality dataset remains a formidable challenge.

heading: C GPT-4 Bias
text: It is acknowledged that using summaries generated exclusively by GPT-4 could introduce biases inherent in its summarization capabilities, it may also be noted that alternatives, such as human evaluation, also carry their own biases. Despite the potential for bias, leveraging GPT-4 for summarization may still be a pragmatic choice, especially in scenarios where access to diverse datasets or sophisticated validation methods is limited. However, in this work, we remain vigilant, recognizing the limitations inherent in both automated and humangenerated summaries, and take proactive steps such as human intervention to validate and contextualise the results to mitigate biases to the best extent possible within the given constraints.

heading: D Prompts
text: We use prompting in three stages - data generation, finetune-inference and critique. There are two kinds of prompts - system prompt and user prompt.

heading: D.1 Data Generation
text: system : You are an AI assistant who are to generate a summary of a medical document specific to a certain persona which can be doctor, patient, normal person. The summary of a medical document should be generated from the perspective of the respective persona. user : Summarize the medical document given below from the perspective of a {persona} [doctor/patient/normal person] and return the summary only. The medical document is as follows: Document: {document}

heading: D.2 Finetune and Inference prompt
text: user prompt - Summarize the medical document given below from the perspective of a persona: ### Document: document

heading: D.3 Critique
text: system: You are an AI assistant who is to evaluate the summary of a medical document specific to a certain persona which can be doctor, patient or a normal person. A doctor requires a detailed and technical summary about the medical document. Patients require a layman’s summary about the medical document, with information about things like causes, effects, treatment etc. that may be helpful to them. A normal person has no medical knowledge and requires a generic summary about the medical document. You need to return a score between 0 and 1 reflecting the quality of the generated summary based on some criteria. user: You are given a medical document and the corresponding summary of the document generated from the perspective of a {persona} predicted by a language model as follows. Document: {document} Ground truth summary : {label summary} Summary from the perspective of a {persona} [doctor/patient/normal person]: {model generated summary} Evaluate the above persona based summary for the document in terms of each of the following criteria and return only a score between 0 and 1 without any explanation:
• The extent to which the generated summary is relevant to the specific persona {persona}[doctor/patient/normal person] based summary of the document.
• The extent to which the generated personabased summary correctly covers all the important key points described in the persona {persona}[doctor/patient/normal person] based summary of the document.
• The extent to which the summary does not contain information specific to all other possible personas {persona_set - persona}[doctor/patient/normal person] based summary.
• Rate the summary from the point of view of the persona – whether the summary is good, average, or bad. A good summary effectively
captures the essential points, presenting them clearly and concisely. It maintains accuracy, encourages reader engagement, and serves as a compelling introduction to the content. An average summary conveys the main points but may lack some clarity or detail, presenting a decent overview without standing out in terms of conciseness or precision. It provides a basic understanding but not from a more refined focused summary and fails to accurately convey the main points, containing inaccuracies or misinterpretations. It is either overly verbose or lacks coherence, making it difficult for the reader to grasp the core information effectively.
• Calculated summary from the point of view of the persona [Good/Bad/Average] [Calculated from 4 with the help of manual annotation]

heading: E Experiments
text: E.1 Varying Training Size Dataset
To understand the effect of training data size on the performance, we vary the WebMD training data for the Llama2-13b model - taking 10-shot (k-shot settings where k=10), 10%, 40% and 70% of the initial training data, and finetune the Llama2-13b model with same parameter and hyper-parameter settings and the five criterias of GPT-4 Critique outcome (in %) are shown in Fig 1. We see that with increasing the dataset size, the performance of Llama2-13b improves in terms of GPT-4 critique and traditional metrics. Even at 40% of the dataset, the model is able to achieve a very good performance. It shows the effectiveness of the Llama213b model. It also infers that even with very little amount of data in 10-shot Llama2-13b can able to generate appropriate persona and aspect based summary.
E.2 Tuning generation parameters during model inference:
We investigate the impact of tuning the max-newtoken and temperature generation parameters on
the performance of our finetuned model during inference. The variation in performance in terms of the five GPT-4 critique based criteria are shown in Figures 2a and 2b respectively. We observe that our model performs the best for a temperature of 0 and performance degrades significantly as we increase the temperature beyond 0.4. Similarly, the best model performance is achieved for a maxnew-token size of 350. We have used NLTK, Spacy, openai (version=0.28), huggingface_hub, torch and transformers python packages for all experiment.

heading: E.3 Time and GPU
text: We experiment on 80GB A100 GPU with GPU clock cycle 210 MHz. The finetuning and inference time of our finetuned models are in Table 6.

heading: F Human Annotations

heading: Annotation Guidelines for Comparative Rating:
text: We provide instructions with explanations of different persona and ask to identify which summary belongs to which persona as shown in Fig 5. We also provide the link of document along with distinct summaries of GPT-4 and Llama2-13b finetune model for comprison as shown in Fig 6. The instructions are the following - “You are given a summary of a medical document specific to the perspective of a certain group of people (doctor, patient and normal person). A doctor requires a detailed and technical summary about the medical document. A patient requires a layman summary about the medical document, with information about things like causes, effects, treatment etc. that may be helpful to him. A patient only requires a top level view of the extensive medical details and not so much
medical details like a doctor. A normal person has no medical knowledge and requires a generic summary about the medical document and does not require extensive medical details.” Annotation Guidelines to Prolific and Doctors
For annotator selection, we have several criterias. Annotator selection includes specific criteria such as ‘Degree subject’ in Health and welfare, ‘Highest education level completed’ as Doctorate degree or Graduate degree, ‘Fluent languages’ in English, ‘Approval Rate’ of 90–100, ‘Subject’ in Medicine, and ‘Employment Sector’ as Doctor. Further annotations are conducted by graduate doctors (details in Section - Human Evaluation Part).
Following is the annotation guideline to ‘Prolific’ annotation platform -
Objective: Generate Personified Summaries by Prolific
Introduction: In this study, you are tasked with generating a summary tailored to three different personas: a Doctor, a Patient, and a Normal Person. You will be provided with a document link containing a Source Document (SD) – which can be a medical research document or a general article related to health from https://www.webmd.com/. . Additionally, a Persona (P) will be present.
Your Task: Read the Source Document (and Persona) and craft three summaries, each targeted towards one of the personas mentioned. Use your understanding and perspective to tailor the information in a way that is most relevant and comprehensible to each persona.
Summary Persona: Doctor Persona: Craft a summary that focuses on medical terminology, guidelines, and provides information suitable for a medical professional. Emphasize technical accuracy and relevance to medical practice. A doctor requires a detailed and technical summary about the medical document.
Patient Persona: Generate a summary with a patient-centric approach, avoiding excessive technical jargon. Ensure that the information is clear, easily understandable, and addresses concerns that a patient might have. A patient requires a nontechnical summary about the medical document, with information about things like causes, effects, treatment etc. that may be helpful to him. A patient only requires a top level view of the extensive medical details and not so much medical details like a doctor.
Normal Person Persona: Tailor a summary for
a general audience without a medical background. Use simple language, avoid technical terms, and present the information in a way that is accessible and engaging to a layperson. A normal person has no medical knowledge and requires a generic summary about the medical document and does not require extensive medical details.
Instructions: 1. Carefully review the Source Document and the Persona. Consider the specific needs and understanding level of each persona while generating the summaries.
2. No additional software download is required. Use a browser, preferably Google Chrome, and ensure a stable internet connection.
3. Allocate time judiciously for crafting each of the three summaries based on the provided 2 SD instances.
4. After completion, you will be asked to provide feedback on the generation exercise, platform interaction, and details about your academic background, age, country of birth, and any medical background or experience with model-generated summaries.
Payment Requirements: Upon completing the study, click on the provided link containing the completion code to redirect you to the Prolific platform. Payment will be processed within one to two weeks.
Ethical Considerations: Adhere to strict confidentiality and data protection standards to ensure the privacy of medical information. If you have concerns or questions, feel free to reach out, as this study aligns with ethical guidelines.
This study aims to harness diverse perspectives, including those of medical professionals, to refine the generation of personified summaries for enhanced utility in various contexts.
Next, the details while providing the documents - “You will be given 2 documents in the next 2 pages and you need to write the summaries with respect to Doctor, Patient and Normal Person (as in example).
Please do not use ChatGPT/GPT-4 or any Large Language Models - all summaries should be generated by human properly. It is a strict instruction and will be checked manually - if found any issue: it will be rejected and re-doing will be required.
Your summary length (word count) is approximately 15% - 20% of the document length (word count) for three different types.”

heading: G Examples
text: Two human annotated examples in doctor persona is shown in Fig 3 where the GPT-4 generated summary is better and Fig 4 where LLAMA-2 generated summary is better. Two examples of human annotation interface is shown in Fig 5 and Fig 6 respectively.

heading: Future Work
text: We plan to extend our work in generating accurate persona-based summaries for documents in other domains such as legal, enterprises, education and more, which is the focus of our future work