Welcome to this comprehensive guide on crafting a better survey paper. Survey papers play a crucial role in summarizing and presenting state-of-the-art knowledge within a specific domain. Whether you are a seasoned researcher looking to refine your survey writing skills or a budding academic endeavouring to embark on your first survey paper journey, these six fundamental steps will serve as your compass in navigating the survey paper writing process.

Introduction

A Researcher begins his research journey by first writing a survey paper in the domain of his research. Writing a survey paper helps a researcher to

i) Understand his domain of research thoroughly

ii) Identify the existing research gaps,

iii) Understand the various parameters and their role in solving the problem and

iv) Infrastructure and Data set requirements for research.

In fact, after completing my survey paper I decided to go for optimal cloud infrastructure for my work. This helped me a lot in cutting the total cost of my research.

A survey paper is also a service to the scientific community. You are doing research for young research scholars. Instead of reading a vast amount of papers to understand what a scientific topic is about,  a researcher just needs to read your paper and can start his research at the earliest with a clear direction in mind.  To make the researcher read your paper, you must have good content and a high citation score. Otherwise, your paper is like purchasing a site in the forest and trying to sell with no one ready to buy.

What is expected in a survey paper?

A survey paper is a research paper which lists and analyses the latest research works in a particular research domain of interest. The survey paper derives some conclusions from the work carried out so far and provides new avenues for future research.

1. Eye-Catching “Title” for the Survey Paper

The primary function of a title is to provide a clear summary of the paper’s content. So keep the title brief and clear. Use active verbs instead of complex noun-based phrases, and avoid unnecessary details.   Moreover, a good title for a research paper is typically around 10 to 12 words long. A lengthy title may seem unfocused and take the readers’ attention away from an important point.  As I supervise many candidates who hail from non-native English-speaking countries, they struggle a lot with writing error-free Titles. So, I always advise them to take English classes in parallel with their research.

A good research paper title should contain keywords used in the manuscript and should define the nature of the study. Think about terms people would use to search for your study and include them in your title.  Do not use abbreviations in the title.  Knowing the search intent of the people who search for the keyword is critical as it helps you to find the top searched keywords. I learnt this technique by mapping SEO-based keyword research techniques to find quality keywords for my title.

Usually, a Title for a survey paper starts with ,   “Recent trends in ….”, “Advances in…..”.

Some survey papers end with “…….: A Survey”.

Here are the other ways to mention survey paper titles:

For more details on writing Titles for your research paper visit my blog post on: Research Paper Title: 03 Simple Steps to Make it Easily Discoverable

2. Giving an Overview of the Survey Paper Through the “Abstract” Section

The abstract is a summary of a research paper describing the problem investigated, the methods applied, the main results and conclusions. Abstracts are a good way to summarise the key contents of a paper, from the research it uses to the ideas you want to share with the reader.

The abstract is a single paragraph containing a minimum of 200 words up to 300 words. An abstract offers a preview that highlights key points and helps the audience decide whether to view the entire work.

3. Highlighting “Key Terms” of the Survey Paper

The purpose of keywords in a research paper is to help other researchers find your paper when they are searching for the topic. Keywords define the field, subfield, topic, research issue, etc. that are covered by the article.

Most electronic search engines, databases, or journal websites use keywords to decide whether and when to display your paper to interested readers. Keywords make your paper searchable and ensure that you get more citations. Thus, it is important to include the most relevant keywords that will help other authors find your paper.

For Example for the abstract written in the previous section, the keywords can be:
Keywords: Plant pathology, bacterial blight, diseased part extraction, Image processing, Soft Computing.

For more details on  identifying the most prominent keywords for your research paper, you can visit my blog post on: Top 10 Rules to Identify Keywords for your Research Paper

4. Building a Strong “Introduction” Section of the Survey Paper

A good introduction in a survey paper explains how the research problem has been solved by various researchers and creates ‘leads’ to make the reader want to delve further into the research domain.  Introduce the terminology of the field and describe what the various terms mean.

The introduction does not have a strict word limit, unlike the abstract, but it should be as concise as possible. The introduction works upon the principle of introducing the paper’s topic and setting it into a broad context, gradually narrowing it down to a research problem.

The main task of the introduction is to set the scene, giving your paper a context and seeing how it fits in with previous research in the field. The first few paragraphs of your introduction can be based on a historical narrative, from the very first research in the field to the current day.

The entire introduction should logically end with the research question. The reader, by the end of the introduction, should know exactly what research issue you are trying to survey with your paper.

Here’s a tabular format of the introduction for your survey paper:

Subsection Headings	Content
Background and Objective	– Objective: Explore advancements in extracting meaningful leaf disease features through image processing techniques.
	– Importance: Significance of the problem and its relevance in the image processing community.
Terminologies and Definitions	– Introducing key terminologies used in the research domain and defining their significance.
Scope and Focus	– Defining the scope of the survey paper and narrowing down to the core research problem.
Historical Narrative	– Presenting a historical perspective, tracing the evolution of image processing techniques for leaf disease identification.
Setting the Context	– Establishing the context of the survey paper within the broader landscape of previous research in the field.
Creating ‘Leads’ for Further Exploration	– Emphasizing the aim to provide readers with insights that encourage them to delve deeper into the research domain.
Research Question	– Concluding the introduction with a clear statement of the central research question to be addressed in the survey paper.
	– Guiding readers towards an exploration of recent advances in diseased part extraction using Soft computing techniques.

Please note that the actual sub-section headings and their order may vary depending on the specific content and focus of your survey paper. The table above provides a general structure based on the information given in the previous response.

Example of an Introduction:

For more details on writing the introduction section, you can visit my blog post on: How to Write an Effective Research Paper Introduction in 03 easy steps?

5. The “Literature Review” of the Survey Paper

The Literature Review has to be based on the specific theme of research which will help the reader in focusing his/her research on specific concepts. In this section, the provided details themes serve as a foundation for your Survey Paper. To create a comprehensive survey paper, it is essential to extend each theme with detailed analysis, research gaps, in-depth block diagrams, functioning descriptions, comparative analysis, and other relevant elements. By thoroughly exploring and analyzing the existing literature, you can enrich the survey paper with critical insights, identify research challenges, and provide valuable contributions to the field. Your thorough examination will contribute to a complete and well-rounded survey paper on the chosen topic.

Some possible  themes can be:

i. Complexity of the problem:

There can be various types of solutions for a  given problem domain and the author has to organize them in the increasing level of complexity or scale.

Example :

Literature Review: Complexity of Scene Analysis in Image Processing

In the field of Image Processing, scene analysis emerges as a core problem, where researchers seek to extract meaningful information from visual data. The solutions for scene analysis can range from simple grayscale images with few objects against a constant background to complex images with multiple objects of varying shapes and colours against diverse backgrounds.

At the basic level of complexity, researchers have explored methods for segmenting simple grayscale images containing only one or two objects of identical shapes against a uniform background. Early studies in the literature focused on techniques like thresholding and edge detection to identify and distinguish objects. (Reference: Smith et al., 2005; Johnson and Brown, 2008)

Moving up the complexity ladder, the literature presents solutions for scenes with multiple objects of different shapes but with a consistent background. Researchers have proposed methods such as region growing and contour tracing to extract relevant objects from such images. (Reference: Lee and Kim, 2010; Chen et al., 2012)

As the complexity further increases, scene analysis encompasses images with diverse objects of varying shapes and colours set against complex backgrounds. In these cases, advanced algorithms like Convolutional Neural Networks (CNNs) and deep learning techniques have been deployed to achieve accurate and robust object recognition. (Reference: Wang et al., 2016; Zhang and Li, 2018).

Additionally, research has expanded into real-world scenarios, where scene analysis encounters challenges such as occlusions, illumination variations, and cluttered backgrounds. Addressing these complexities, the literature explores techniques like scale-invariant feature transform (SIFT) and histogram of oriented gradients (HOG) to handle object detection and recognition in challenging environments. (Reference: Liu et al., 2019; Park and Lee, 2020)

The progression of complexity in scene analysis solutions reveals the evolution of Image Processing techniques to accommodate real-world challenges. By organizing the literature review based on the increasing level of complexity, this survey paper aims to assist readers in understanding the advancements made in addressing scene analysis across diverse image scenarios.

ii. Static vs. Dynamic:

Many fields can be organized by static techniques, dynamic techniques, and even hybrid. For example Static or Dynamic Routing in Computer Networking.

Example :

Literature Review: Static vs. Dynamic Resource Allocation in Cloud Computing

iii. Segregating the Design Space:

Many systems are made up of components, so maybe for a  computer network paper, the author could divide the problems into a physical layer, application layer, session layer, transport layer, data link layer and physical layer.

Literature Survey: Segregating the Design Space in AI-based Recommender Systems

In recent years, Artificial Intelligence (AI) has driven significant advancements in recommender systems, revolutionizing personalized recommendations across various domains. To comprehensively analyze the design space of AI-based recommender systems, researchers have categorized these systems into different architectural layers, each responsible for specific aspects of recommendation.

At the Data Collection and Preprocessing Layer, researchers focus on gathering and preprocessing vast amounts of user data to build comprehensive user profiles. Techniques like collaborative filtering and content-based filtering have been explored to analyze user preferences and item characteristics (Reference: Smith et al., 2022; Johnson and Brown, 2023).

Moving up the architectural layers, the Feature Engineering and Representation Learning Layer aims to extract meaningful features and embeddings from the data. Deep Learning models like Neural Collaborative Filtering (NCF) and Transformer-based architectures have gained attention for their ability to learn rich representations from user-item interactions (Reference: Lee and Kim, 2021; Chen et al., 2022).

The Recommendation Algorithm Layer is responsible for developing algorithms that generate personalized recommendations based on user preferences. Recent advancements include hybrid recommendation techniques that combine collaborative and content-based filtering, as well as reinforcement learning approaches for sequential recommendation tasks.

At the Interpretability and Fairness Layer, researchers focus on ensuring transparency and fairness in recommender system outputs. Explainable AI (XAI) techniques and fairness-aware recommendation algorithms have been studied to provide users with insights into the reasons behind recommendations and mitigate potential biases (Reference: Wang et al., 2022; Zhang and Li, 2023).

The Deployment and Evaluation Layer involves the real-world implementation of AI-based recommender systems. Researchers investigate methods to deploy models at scale while considering resource constraints and latency requirements. Moreover, the evaluation of recommender systems includes metrics such as accuracy, diversity, and serendipity to assess overall performance and user satisfaction.

By segregating the design space of AI-based recommender systems into these distinct architectural layers, this survey paper aims to provide readers with an organized understanding of the state-of-the-art approaches in personalized recommendations. These architectural layers serve as a structured framework for exploring the latest advancements and challenges in AI-driven recommendation technologies.

Please note that the references used in this example are imaginary

iv. Major Approaches in a Specific Domain:

6. The “Conclusion” Section of the Survey Paper

The conclusion must answer the queries presented by your survey goals and objectives. The conclusion must be written in an interesting yet academic manner.  No emotions should be attached to your conclusions but a commentary in the third person is required.  Being the final portion of your survey paper, the conclusion serves as the researcher’s final say on the subject of the survey.

The conclusion must be a synthesis of the survey results with

i) an interpretation of each result

ii) the proposal of a course of action based on the result and

iii) a solution to the issues that emerged from the survey.

The tone of the conclusion should match that of the results and the rest of the data collection process.  The conclusion should be able to wrap up the entire survey from the formulation of survey goals up to the satisfaction of such objectives.

Example:

After roughly two decades of research on leaf image analysis for pathological issues,  many elements of pathological issues associated with leaves are well understood. In particular, accurate and efficient algorithms for leaf-diseased spot extraction are now well known.

As a result, during the past few years, we have seen the focus turn from the fundamentals of disease spot extraction to more difficult problems such as, the type of the leaf disease and the stage of the leaf disease.  Few algorithms in this context are available. However, a comprehensive evaluation and comparison of these more advanced algorithms has yet to be done.

One of our goals in this review is to consolidate existing quantitative results and to carry out comparative analyses. We believe that much of the leaf image analysis for pathological work in the coming decade should and will be bolstered by more complete quantitative performance evaluations. The recent article by Wimar [10] is a promising first step.

Perhaps the most practically significant advance in the last decade has been the appearance of machine learning algorithms. However current implementation of Machine learning algorithms is still relatively simplistic. More demanding potential applications require algorithms to be very precise and reliable. This remains a challenging research topic that we predict will see progress in the coming decade.

For further details on writing the conclusion section, you can visit my blog post on : Art of Writing Conclusion Section to your Research Paper

Before We Close….

While writing a blog post, I realized I couldn’t cover everything about crafting survey papers. That’s where “WRITING LITERATURE SURVEY PAPER: A STEP BY STEP GUIDE” comes in. It’s an incredible book that simplifies the writing process using a single example, “The Role of Artificial Intelligence in Healthcare Diagnostics.” Imagine having a friendly mentor explaining each step. Although a blog post can’t teach everything, this book breaks it down so you can become a survey paper writing expert.

Conclusion

A survey paper plays a crucial role in providing readers with a systematic overview of existing research, methodologies, and advancements in a specific field. By adhering to the outlined steps, the survey paper can effectively convey valuable insights and contribute to the understanding of the chosen research domain.

Frequently Asked Questions: