related work - chunhualiao/public-docs GitHub Wiki

related work:ai generation

  • often having some rough idea/direction, and the approach to use
  • then deep research to survey the landscape
  • next : what to explore for a new paper or research direction?

For a paper or proposal's related work section

  • how can one evaluate its quality?

I think there should be a grading rubric of multiple metrics.

build a graph of a set of related work

  • papers are connected via many different relations: what to show?
  • When define a graph, need define the types of nodes and edges allowed and their semantics
    • how to find all and important relations among papers in related work?

Graph to text conversion

  • if we already have a citation graph with papers and their relations, what would be the best way to automatically traverse them and subsequently convert to a related work section text? Would there be different traverse orders and conversion rules that result in different flavors of related work section writing styles?

Evaluating the Quality of a Related Work Section: A Rubric-Based Approach

In any research paper or proposal, the related work section serves as a critical foundation. It shows that the authors are aware of the existing body of knowledge, have identified gaps, and are building on—or deviating meaningfully from—prior work. But how can one systematically evaluate the quality of this section?

We argue that a grading rubric of multiple metrics provides a structured and transparent way to assess related work sections. Much like how instructors use rubrics to grade essays or projects, researchers, reviewers, and even automated tools can benefit from a clearly defined set of evaluation criteria.

Why a Rubric?

A grading rubric promotes consistency, reduces subjectivity, and makes feedback actionable. Instead of vague comments like “needs more depth” or “too many citations,” a rubric makes it possible to identify specific strengths and weaknesses. It also helps authors self-assess and improve their drafts before submission.

Our proposed rubric evaluates related work along five key dimensions:

  1. Relevance
  2. Coverage
  3. Contextualization
  4. Differentiation
  5. Clarity

Each dimension captures a distinct aspect of quality and can be scored individually to produce an overall assessment.

The Five Metrics Explained

1. Relevance: Are the cited works directly tied to the paper’s core topic?

High-quality related work sections stay focused. Every cited paper should be meaningfully connected to the current research problem. Including tangential or outdated references signals either a lack of understanding or an attempt to pad the bibliography.

  • Excellent (5): All works are tightly aligned with the research problem.
  • Average (3): Some papers are peripherally related or outdated.
  • Poor (1): Many citations are irrelevant or included without justification.

2. Coverage: Does the section adequately represent the key literature?

Coverage involves both breadth (including work from various schools of thought or subfields) and depth (engaging sufficiently with the most important prior works).

  • Excellent (5): Covers all major papers and recent developments; no obvious gaps.
  • Average (3): Omits some significant works; few recent references.
  • Poor (1): Misses widely known or highly cited papers; narrow view.

3. Contextualization: Does it explain how cited works relate to each other and to the current work?

This metric evaluates whether the section builds a narrative or just lists studies. A strong section places prior work into a conceptual framework and guides the reader toward understanding how the current research fits in.

  • Excellent (5): Synthesizes prior work into themes, trends, or competing approaches.
  • Average (3): Mostly descriptive; weak narrative thread.
  • Poor (1): No contextualization; reads like an annotated bibliography.

4. Differentiation: Is it clear how the current work advances beyond prior work?

A common reviewer complaint is “unclear novelty.” A good related work section doesn’t just summarize others—it explicitly shows how the current research is different or better. This sets up the reader for a compelling problem statement or hypothesis.

  • Excellent (5): Clearly states the gaps or limitations in prior work and how the new work addresses them.
  • Average (3): Hints at differences but lacks direct comparison.
  • Poor (1): No clear contrast; the novelty is left ambiguous.

5. Clarity: Is the writing precise, organized, and accessible?

Even if all the right content is present, poor organization or jargon-filled prose can undermine a related work section. Clear subheadings, topic sentences, and transitions make a big difference.

  • Excellent (5): Well-structured; easy to follow; minimal jargon.
  • Average (3): Understandable but may be dense or repetitive.
  • Poor (1): Disorganized, confusing, or poorly written.

Sample Rubric Table

Metric 1 – Poor 3 – Average 5 – Excellent
Relevance Many irrelevant citations Some tangential works included All works directly relate to core topic
Coverage Omits key papers; few recent sources Includes some major works Comprehensive coverage; recent and influential papers
Contextualization No synthesis; list of papers Describes some relationships Thematic synthesis; papers build toward problem setup
Differentiation No clear novelty Implicit contrast only Gaps and distinctions clearly stated
Clarity Hard to follow; technical jargon Mostly readable Clear, well-structured, and logically ordered

Each dimension can be scored from 1 to 5, giving a maximum score of 25. A score above 20 suggests an excellent related work section. A score below 15 indicates room for major improvements.

Applying the Rubric: A Case Example

Consider a draft related work section for a paper on self-supervised learning for medical image segmentation.

  • It cites major works like SimCLR and MoCo, but neglects recent domain-specific advances.
  • It describes each paper in isolation, without explaining trends or themes.
  • It does not clearly state how its method is different from MoCo-based pipelines.
  • The writing is formal but lacks topic sentences and logical flow.

Applying the rubric:

  • Relevance: 5 (all papers are on topic)
  • Coverage: 2 (omits recent domain-specific work)
  • Contextualization: 2 (no synthesis)
  • Differentiation: 2 (novelty unclear)
  • Clarity: 3 (readable but poorly organized)

Total Score: 14/25 — this suggests the section is functional but needs significant revision.

Benefits for Authors, Reviewers, and Tools

This rubric-based approach offers practical benefits:

  • Authors: Can self-assess and iterate before submission.
  • Reviewers: Gain a structured way to justify scores and feedback.
  • Tools: Automated analysis (e.g., citation graph coverage, novelty detection via LLMs) can be aligned with these metrics.

As academic writing becomes increasingly assisted by AI, this rubric provides a human-centered framework that can guide both manual and machine-supported evaluation.

Conclusion

A strong related work section is not just a requirement—it's an opportunity to demonstrate expertise, justify your contribution, and build reader confidence. By evaluating this section along multiple dimensions—Relevance, Coverage, Contextualization, Differentiation, and Clarity—authors and reviewers can ensure that the section serves its true purpose: to situate the new work in the ongoing scholarly conversation.

Using a rubric is more than a checklist—it is a lens that reveals the structure and effectiveness of academic communication. As research grows more interdisciplinary and fast-moving, having a reliable way to assess and improve related work sections becomes not just helpful, but essential.

Would you like a downloadable version of this rubric as a PDF or spreadsheet template?