% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.

\documentclass[11pt]{article}

% Change "review" to "final" to generate the final (sometimes called camera-ready) version.
% Change to "preprint" to generate a non-anonymous version with page numbers.
\usepackage[final]{acl}

% Standard package includes
\usepackage{times}
\usepackage{latexsym}
\usepackage{amsthm}
\usepackage{natbib}%
% \bibliography{anthology,custom}
% For proper rendering and hyphenation of words containing Latin characters (including in bib files)
\usepackage[T1]{fontenc}
% For Vietnamese characters
% \usepackage[T5]{fontenc}
% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets

% This assumes your files are encoded as UTF8
\usepackage[utf8]{inputenc}

% This is not strictly necessary, and may be commented out,
% but it will improve the layout of the manuscript,
% and will typically save some space.
\usepackage{microtype}
\usepackage{multirow} 
% This is also not strictly necessary, and may be commented out.
% However, it will improve the aesthetics of text in
% the typewriter font.
\usepackage{inconsolata}

%Including images in your LaTeX document requires adding
%additional package(s)
\usepackage{graphicx}
\usepackage{tcolorbox}
% If the title and author information does not fit in the area allocated, uncomment the following
%
%\setlength\titlebox{<dim>}
%
% and set <dim> to something 5cm or larger.


\title{MedSummRAG: Domain-Specific Retrieval for Medical Summarization}

% Author information can be set in various styles:
% For several authors from the same institution:
% \author{Author 1 \and ... \and Author n \\
%         Address line \\ ... \\ Address line}
% if the names do not fit well on one line use
%         Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\
% For authors from different institutions:
% \author{Author 1 \\ Address line \\  ... \\ Address line
%         \And  ... \And
%         Author n \\ Address line \\ ... \\ Address line}
% To start a separate ``row'' of authors use \AND, as in
% \author{Author 1 \\ Address line \\  ... \\ Address line
%         \AND
%         Author 2 \\ Address line \\ ... \\ Address line \And
%         Author 3 \\ Address line \\ ... \\ Address line}

\author{Guanting Luo \\
  The University of Osaka \\
  \texttt{guanting.luo@ist.osaka-u.ac.jp} \\\And
  Yuki Arase \\
  Institute of Science Tokyo \\
  \texttt{arase@c.titech.ac.jp} \\}

%\author{
%  \textbf{First Author\textsuperscript{1}},
%  \textbf{Second Author\textsuperscript{1,2}},
%  \textbf{Third T. Author\textsuperscript{1}},
%  \textbf{Fourth Author\textsuperscript{1}},
%\\
%  \textbf{Fifth Author\textsuperscript{1,2}},
%  \textbf{Sixth Author\textsuperscript{1}},
%  \textbf{Seventh Author\textsuperscript{1}},
%  \textbf{Eighth Author \textsuperscript{1,2,3,4}},
%\\
%  \textbf{Ninth Author\textsuperscript{1}},
%  \textbf{Tenth Author\textsuperscript{1}},
%  \textbf{Eleventh E. Author\textsuperscript{1,2,3,4,5}},
%  \textbf{Twelfth Author\textsuperscript{1}},
%\\
%  \textbf{Thirteenth Author\textsuperscript{3}},
%  \textbf{Fourteenth F. Author\textsuperscript{2,4}},
%  \textbf{Fifteenth Author\textsuperscript{1}},
%  \textbf{Sixteenth Author\textsuperscript{1}},
%\\
%  \textbf{Seventeenth S. Author\textsuperscript{4,5}},
%  \textbf{Eighteenth Author\textsuperscript{3,4}},
%  \textbf{Nineteenth N. Author\textsuperscript{2,5}},
%  \textbf{Twentieth Author\textsuperscript{1}}
%\\
%\\
%  \textsuperscript{1}Affiliation 1,
%  \textsuperscript{2}Affiliation 2,
%  \textsuperscript{3}Affiliation 3,
%  \textsuperscript{4}Affiliation 4,
%  \textsuperscript{5}Affiliation 5
%\\
%  \small{
%    \textbf{Correspondence:} \href{mailto:email@domain}{email@domain}
%  }
%}
\usepackage{tikz}
\usepackage{fontawesome5}
\usepackage{xcolor}
\usepackage{booktabs}

\definecolor{myBlue}{HTML}{1F77B4}
\definecolor{myOrange}{HTML}{FF7F0E}
\definecolor{myPurple}{HTML}{9B4D96}
\definecolor{myGreen}{HTML}{A8D700}
\definecolor{myGrey}{HTML}{D9D9D6}
\definecolor{myYellow}{HTML}{F7E38E}
\begin{document}
\usetikzlibrary{shapes.geometric, arrows, positioning, calc}
\maketitle
\begin{abstract}
Medical text summarization faces significant challenges due to the complexity and domain-specific nature of the language. Although large language models have achieved significant success in general domains, their effectiveness in the medical domain remains limited. This limitation stems from their insufficient understanding of domain-specific terminology and difficulty in interpreting complex medical relationships, which often results in suboptimal summarization quality. To address these challenges, we propose MedSummRAG, a novel retrieval-augmented generation (RAG) framework that integrates external knowledge to enhance
summarization.
Our approach employs a fine-tuned dense retriever, trained with contrastive learning, to retrieve relevant documents for medical summarization.
The retrieved documents are then integrated with the input text to generate high-quality summaries.
Experimental results show that MedSummRAG achieves significant improvements in ROUGE scores on both zero/few-shot and fine-tuned language models, outperforming baseline methods. 
These findings underscore the importance of RAG and domain adaptation of the retriever for medical text summarization.
The source code of this paper can be obtained from: \url{https://github.com/guantingluo98/MedSummRAG}
\end{abstract}

\section{Introduction}

Medical text summarization is a crucial task for helping medical practitioners and patients,
aiming to distill complex and information-dense medical documents into concise, accurate, and clinically useful summaries \citep{xie2023surveybiomedicaltextsummarization}. 
This task is particularly challenging due to the specialized nature of medical language and the presence of domain-specific terminologies \citep{chaves2022automatic}. Traditional summarization models often struggle in this domain, as they may fail to generate satisfactory summaries.

With the rise of large language models (LLMs), significant advancements have been made in general-domain summarization \citep{pu2023summarizationalmostdead}. 
However, medical summarization presents unique challenges, such as domain-specific terminology and complex relationships, which generic LLMs struggle to address effectively.
LLMs trained on broad-domain corpora tend to overlook key medical concepts, misinterpret medical abbreviations, and produce hallucinated content that could mislead practitioners and researchers \citep{li-etal-2024-better, hosseini2024leveraging}. 
These limitations highlight the need for models that can effectively incorporate external domain knowledge. By leveraging external knowledge documents, such as healthcare question-answer pairs, models can better understand domain-specific concepts, reduce errors, and generate high-quality summaries.

In this work, we propose MedSummRAG (Medical Summarization with Retrieval-Augmented Generation), a novel retrieval-augmented generation (RAG) framework designed specifically for medical text summarization. By leveraging external medical knowledge, MedSummRAG enhances the quality of generated summaries. Our approach employs a fine-tuned dense retriever, trained using contrastive learning \citep{oord2019representationlearningcontrastivepredictive}, to effectively identify domain-relevant documents.

The key contribution of our work is the novel RAG framework for medical text summarization. Our approach improves retrieval quality by leveraging contrastive learning that employs synthetic positive samples generated using an LLM. This enables the framework to effectively identify domain-relevant documents, improving the overall quality of generated summaries. 
We conduct experiments to investigate the effectiveness of MedSummRAG. Our results demonstrate consistent improvements measured by ROUGE scores in multiple configurations: both on zero/few-shot and fine-tuned language models. 

\begin{figure*}[t]
    \centering
    \begin{tikzpicture}[scale=0.8, transform shape,
        node distance=1.5cm and 1.5cm,
        every node/.style={draw, minimum width=2cm, minimum height=1cm, align=center},
        arrow/.style={->, thick},
        blackarrow/.style={->, thick, black},    
        orangearrow/.style={->, thick, orange},  
        bluearrow/.style={->, thick, blue}       
    ]

    \node[rounded corners, text width=4.5cm] (A) {\textbf{Medical Text} \\
    {\fontsize{7pt}{8pt} 23 surgeries and counting......lower lip birthmark, have tried all options out there and guess what still have it, continues to grow back.....any suggestions? Is there a cure coming in the next few years hopefully?}};
    \node[right=1 cm of A, rounded corners, fill = myYellow] (B) {Sparse \\ Retriever};
    \node[right=1 cm of B, rounded corners, fill = myYellow] (C) {Dense \\ Retriever};
    \node[above= 1 cm of B, draw=none] (D) {\small Knowledge Base \\ \huge\faDatabase };
    \node[right=1 cm of C, draw=none] (F) {
    \huge\textcolor{myPurple}{\faRobot} \\ \small
    Summary  \\ \small Generator};
    \node[right=0.7 cm of F, rounded corners, text width=3 cm] (G) {\textbf{Summary} \\
    {\fontsize{7pt}{8pt} Are there any new treatments or cures for a persistent lower lip birthmark?}};
    \node[below= 1 cm of C, draw=none] (H) { \huge\textcolor{myBlue}{\faRobot} \\ \small Positive Sample
    \\ \small Generator}; 
    \node[left=1 cm of H, rounded corners, fill = myGrey] (E) {Training Set};

    \draw[blackarrow, line width=0.5mm] (A) -- (B); % Input 
    \draw[blackarrow, line width=0.5mm] (D) -- (B); % 
    \draw[blackarrow, line width=0.5mm] (B) -- (C); 
    \draw[color=myOrange, line width=0.5mm, ->] ($(A.east) + (0,1)$) -| ++(6.5,0) |- (F.west); 
    \draw[color=myOrange, line width=0.5mm, ->] (C) -- (F); 
    \draw[color=myOrange, line width=0.5mm, ->] (F) -- (G); 


    \draw[dashed, color=myBlue, line width=0.5mm, ->] (B.south) -- ++(0,-0.5) -- ($(C.south) + (0,-0.5)$); 
    \draw[color=myBlue, line width=0.5mm, ->] (E) -- ($(E.east) + (1.1,0)$); 
    \draw[color=myBlue, line width=0.5mm, ->] (H) -- (C); % 

    \end{tikzpicture}
    \caption{Overview of our MedSummRAG framework. Black arrows indicate retrieving relevant documents by sparse and dense retrievers.  Blue dashed arrow represents the negative sample extraction, while solid blue arrows show the generation of synthetic positive samples. Both positive and negative samples are used to fine-tune the dense retriever. Orange arrows show the concatenation of medical text and the retrieved document as input to the summary generator, producing the summary.}
    \label{fig:rag}
\end{figure*}
\section{Related Work}

Medical summarization has been a long-standing research problem due to its critical role in supporting clinical decision-making and healthcare planning. With the rise of pre-trained language models, significant progress has been made in medical summarization. Pre-trained language models have demonstrated the ability to generate medical summaries, such as doctor-patient conversation summaries, by utilizing knowledge derived from pretrained models \citep{zhang-etal-2021-leveraging-pretrained}.
\citet{balde2024medvoc} proposed MEDVOC, a dynamic vocabulary adaptation strategy that optimizes pre-trained language models' vocabulary for medical text summarization, achieving improvements in high Out-Of-Vocabulary settings.

Despite the progress enabled by pre-trained models in medical summarization, their pre-trained knowledge may be insufficient for handling specific downstream tasks. RAG offers a promising solution by integrating external knowledge to enhance the overall quality of generations \citep{fan2024survey}. Recent studies have demonstrated the potential of RAG in various domains, such as decision-making tasks \citep{lee-etal-2024-planrag}; question answering \citep{jeong-etal-2024-adaptive}; and radiology report generation \citep{xia-etal-2024-rule}. Although RAG has demonstrated success in various domains, its application to medical summarization remains underexplored. Our work aims to propose a RAG framework specifically adapted for medical summarization to generate high-quality summaries.

\section{Proposed Method}

The overall workflow of our approach is illustrated in Figure~\ref{fig:rag}. 
The proposed method consists of document retrieval (Section~\ref{sec:retrieveal}) and summary generation (Section~\ref{sec:generation}). 
For improving the retrieval step to adapt to the medical domain, we employ contrastive learning with synthetic data (Section~\ref{sec:cl}). 

\subsection{Document Retrieval}
\label{sec:retrieveal}

We employ the BM25 \citep{robertson2009probabilistic, bm25s} ranking function to retrieve an initial set of candidate documents based on lexical similarity to the input text. 
This sparse retrieval method serves to reduce the computational cost of subsequent dense retrieval by narrowing down the search space to a manageable set of candidate documents. 

A dense retriever then re-ranks the highly-ranked documents retrieved by the sparse retriever and selects the most relevant document for the generation stage. This step should ensure that the retrieved document is lexically and semantically aligned with the input text to provide useful knowledge for summarization.

\subsection{Generation}
\label{sec:generation}
The generation stage produces summaries based on the input text and the retrieved document. Following the approach of \citet{NEURIPS2020_6b493230}, we simply concatenate the retrieved document with the input text and feed the combined input into a language model. 
The generator 
is expected to produce coherent and factually accurate summaries, leveraging both the input text and the external knowledge provided by the retrieved document.

\subsection{Domain Adaptation of Retriever}
\label{sec:cl}

The retrieval stage aims to identify the most relevant document from a knowledge base to enhance the summarization process. Although existing RAG methods have shown great success in question-answering tasks \citep{asai2023selfraglearningretrievegenerate, xiong-etal-2024-benchmarking}, they often struggle to identify documents that are truly useful for medical text summarization. This is because pre-trained dense retrievers lack the ability to understand what document structures and content are beneficial for enhancing summarization in the medical domain.

The key challenge in fine-tuning retrievers for medical summarization is the lack of training data. 
To address this challenge, we leverage an LLM to generate synthetic positive samples that capture the structural and contextual patterns useful for summarization. Specifically, for each text-summary pair in a training set of summarization, we prompt the LLM to generate a synthetic pair that may enhance medical summarization. 
Table~\ref{tab:prompt} shows the prompt we used. 

\begin{table}[t!]
\centering
\begin{tabular}{p{0.9\linewidth}}
\toprule
You're a retrieval augmented generation assistant, skilled in generating retrieval targets for auto summarization via RAG.
Here is the input-summary pair from a training set: \\
INPUT: \{Train set input text\}\\
SUMMARY: \{Train set summary\}\\
Please help me with generating one fake retrieved question-answer document that would be useful for training a dense retrieval model for automatic summarization via retrieval augmented generation. The fake retrieved document should have this kind of format: \\
QUESTION: \\ 
ANSWER: 
\bottomrule
\end{tabular}
\caption{Promt for synthetic sample generation}
\label{tab:prompt}
\end{table}

We then fine-tune the dense retriever using contrastive learning, inspired by the work of \citet{huang-etal-2023-retrieval}, which improves its ability to retrieve documents relevant to medical summarization. 
For negative samples, we randomly select low-ranked documents by the sparse retriever that should be less relevant to the input text. We optimize the retrieval model using the InfoNCE loss \citep{oord2019representationlearningcontrastivepredictive}.

%

\begin{table*}
    \centering
    \small
    \label{tab:results}
    \begin{tabular}{l|l|ccc}
        \toprule
        \textbf{Base Model} & \textbf{Setting} & \textbf{ROUGE-1} & \textbf{ROUGE-2} & \textbf{ROUGE-L} \\
        \midrule
        \multirow{3}{*}{\shortstack{BioBART-large\\(Standard Fine-tuned)}} & Baseline       & 41.22 & 23.17 & 38.79 \\
                                     & + Naive RAG    & 42.19 & 22.95 & 38.79 \\
                                     & + Fine-tuned RAG      & \textbf{44.50} & \textbf{24.58} & \textbf{41.19} \\
        \midrule
        \multirow{3}{*}{\shortstack{Qwen-2.5-7B-Instruct\\(1-shot Prompting)}} & Baseline       & 34.97 & 13.85 & 32.82 \\
                                     & + Naive RAG    & 38.53 & 16.42 & 33.61 \\
                                     & + Fine-tuned RAG      & \textbf{39.45} & \textbf{17.59} & \textbf{34.60} \\
        \midrule
        \multirow{3}{*}{\shortstack{Qwen-2.5-7B-Instruct\\(2-shot Prompting)}} & Baseline       & 38.15 & 16.34 & 33.82 \\
                                     & + Naive RAG    & 39.89 & 18.00 & 35.28 \\
                                     & + Fine-tuned RAG      & \textbf{40.27} & \textbf{18.30} & \textbf{35.95} \\
        \midrule
        \multirow{3}{*}{\shortstack{Qwen-2.5-7B-Instruct\\(LoRA Fine-tuned)}}   & Baseline       & 42.21 & 21.99 & 38.84 \\
                                     & + Naive RAG    & 42.56 & 21.80 & 39.32 \\
                                     & + Fine-tuned RAG      & \textbf{42.95} & \textbf{22.82} & \textbf{40.03} \\
        \bottomrule
    \end{tabular}
    \caption{\label{results1}
    Performance comparison of different base models on the CHQ-Summ dataset. Results demonstrate the effectiveness of our method across various models, few-shot scenarios, and fine-tuning strategies.
  }
\end{table*}

\section{Experiments}

\subsection{Evaluation Dataset \& Knowledge Base}

\noindent \textbf{Evaluation Dataset:} We use the CHQ-Summ dataset \citep{yadav2022chqsummdatasetconsumerhealthcare}, which consists of consumer health questions formulated by non-experts, paired with brief summaries of the corresponding questions. The questions are sourced from Yahoo! Answers L6 corpus\footnote{\url{https://webscope.sandbox.yahoo.com/catalog.php?datatype=l&did=11}}. The dataset contains $1,000$ training samples, $107$ validation samples, and $400$ test samples.
We evaluate the performance of our method using ROUGE \citep{lin-2004-rouge} scores, including ROUGE-1, ROUGE-2, and ROUGE-L.

\noindent \textbf{Knowledge Base:} We construct the knowledge base using Yahoo! Answers L6 corpus, which contains more than $4$ million question-answer pairs. Each document in the knowledge base represents a single question-answer pair. The content covered in this corpus extends far beyond the scope of healthcare and medicine, encompassing a wide range of topics. 
To prevent data leakage, we exclude all question-answer pairs that overlap with the CHQ-Summ dataset.


\subsection{Implementation Details}

We employed BM25 for sparse retrieval, which retrieved the top $150$ documents for each input text. 
We employed the BGE-M3 \citep{chen2024bgem3embeddingmultilingualmultifunctionality} model as the base dense retriever. For contrastive learning, the positive samples were generated by a frozen Qwen-2.5-7B-Instruct model\footnote{\url{https://qwenlm.github.io/blog/qwen2.5/}}, while the negative samples were constructed by randomly sampling $3$ documents from the BM25-ranked documents in the range of positions $101$ to $150$ for each training sample. 
The BGE-M3 model was fine-tuned for $5$ epochs with a total batch size of $8$. After fine-tuning, the BGE-M3 model re-ranked the top $20$ documents retrieved by BM25 and selected the top $1$ document for the generator.\\

\subsection{Experiment Settings}
To evaluate the effectiveness of our MedSummRAG approach, we conducted four sets of experiments with different generator settings: standard fine-tuning, few-shot prompting, and Low Rank Adapters (LoRA) \citep{hu2022lora} fine-tuning on different language models.
Specifically, we employed 
(1) BioBART-large \citep{yuan-etal-2022-biobart}: the model has shown its strong performance in medical text generation tasks. BioBART-large was first fine-tuned using the training set without RAG, followed by the second stage of fine-tuning with RAG. 
Each fine-tuning consisted of $20$ epochs with a batch size of $8$.
We also experimented with (2) Qwen-2.5-7B-Instruct with One-Shot Prompting, 
(3) Qwen-2.5-7B-Instruct with Two-Shot Prompting, and 
(4) Qwen-2.5-7B-Instruct with LoRA Fine-Tuning \citep{hu2022lora}: the model was fine-tuned using LoRA for $10$ epochs with a batch size of $8$. LoRA fine-tuning was performed with a rank of 8, alpha of 16, and no dropout.
The details of the prompts are described in example~\ref{ex:2} and example~\ref{ex:3}

In all settings, the baseline is the corresponding fine-tuned model or a few-shot prompted models without RAG. 
In addition, we also compared to a naive RAG where the retriever has not been fine-tuned, i.e., without domain adaptation. 
All the experiment was conducted on a single NVIDIA A6000 48G GPU.

\subsection{Results}

In this section, we highlight the key contribution of our RAG-enhanced approach, demonstrating its effectiveness across different models, few-shot settings, and fine-tuning strategies. A consistent performance gap between naive RAG and fine-tuned RAG underscores the importance of domain-adaptive retrieval. This contrast suggests that synthetic examples play a key role in improving the relevance of the retrieval and the overall quality of the summary.

For the standard fine-tuned BioBART-large model, our method significantly improves performance. With naive RAG, only the ROUGE-1 score shows a marginal improvement, while the ROUGE-2 score slightly decreases, and the ROUGE-L score remains unchanged. However, with MedSummRAG, the BioBART-large model achieves a notable increase in ROUGE scores, highlighting the effectiveness of integrating external knowledge through domain-adapted retriever.

For the Qwen-2.5-7B-Instruct model in few-shot prompt settings, our method consistently enhances performance without fine-tuning the generator. Even with naive RAG, we observe modest improvements in ROUGE scores. Fine-tuning the RAG component further boosts performance, demonstrating the effectiveness of our method even when the generator is frozen. Additional prompt examples also contribute to improved results.

For the LoRA fine-tuned Qwen-2.5-7B-Instruct model, integrating naive RAG yields marginal improvements in ROUGE-1 and ROUGE-L, while ROUGE-2 experiences a slight decline compared to the baseline. 
In contrast, our domain-adapted RAG enhances performance across all ROUGE metrics, demonstrating the importance of optimizing the retrieval process to effectively leverage external knowledge in the LoRA fine-tuning setting.

\section{Conclusion}
\label{sec:bibtex}
 
Our experimental results highlight the effectiveness of leveraging external knowledge for adapting language models to medical summarization tasks, addressing the challenge of domain adaptation in specialized medical contexts.
Future work includes extending our approach to a larger-scale knowledge base to further enhance retrieval effectiveness. Additionally, beyond ROUGE-based evaluation, incorporating human evaluation could provide deeper insights into the quality of generated summaries. Furthermore, exploring the application of our fine-tuned RAG framework to other medical summarization tasks, such as radiology report summarization, is another promising direction for advancing our work.

\section*{Limitations}

While our proposed method demonstrates promising results in improving medical text summarization, its generalizability remains to be validated. Our experiments are conducted exclusively on the CHQ-Summ dataset, which focuses on summarizing customer health questions. While this dataset provides a valuable benchmark for medical question summarization, it does not fully represent the diversity of medical texts, such as clinical notes, or discharge summaries.
In addition, while the Yahoo! Answers L6 corpus offers broad coverage, it may contain content of varying accuracy, which motivates future exploration of more medically curated sources to further reduce hallucination risks.

\section*{Acknowledgements}
This work was supported by Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” Grant Number JPJ012425.

\bibliographystyle{acl_natbib} 
\bibliography{anthology,custom}  
\clearpage
\appendix
\section{Appendix}
\label{sec:appendix}

This appendix shows the prompts used for 
summary generation methods described in this paper.


\theoremstyle{definition}
\newtheorem{example}{Example}[section] 

For few-shot learning setting we randomly select samples from the training set. The example~\ref{ex:2} shows the prompt template we use for generating medical summaries in one-shot setting.

\theoremstyle{definition}
\begin{example} \label{ex:2}
You are a helpful assistant. Your task is to summarize the given question based on the provided question and possibly helpful retrieved document. The retrieved document may or may not be useful for summarization. \\
Example:\\
QUESTION: \{Example input text\} \\
RETRIEVED DOCUMENT: \{Example retrieved document\} \\
SUMMARY: \{Example summary\} \\
QUESTION: \{Test set input text\} \\
RETRIEVED DOCUMENT: \{Test set retrieved document\} \\
SUMMARY: 
\end{example}

The example~\ref{ex:3} shows the prompt template we use for generating medical summaries in two-shot setting.

\theoremstyle{definition}
\begin{example} \label{ex:3}
You are a helpful assistant. Your task is to summarize the given question based on the provided question and possibly helpful retrieved document. The retrieved document may or may not be useful for summarization. \\
Examples:\\
QUESTION: \{Example input text\} \\
RETRIEVED DOCUMENT: \{Example retrieved document\} \\
SUMMARY: \{Example summary\} \\
QUESTION: \{Example input text\} \\
RETRIEVED DOCUMENT: \{Example retrieved document\} \\
SUMMARY: \{Example summary\} \\
QUESTION: \{Test set input text\} \\
RETRIEVED DOCUMENT: \{Test set retrieved document\} \\
SUMMARY: 
\end{example}

\end{document}
