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Alex Spinov
Alex Spinov

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Semantic Scholar Has a Free API — It Gives You AI Summaries of Research Papers

#ai #python #api #machinelearning

Most academic search tools give you a title, abstract, and a DOI. Semantic Scholar gives you something different: AI-generated TLDR summaries for every paper.

What Makes It Different

Semantic Scholar is built by the Allen Institute for AI (AI2). Unlike Crossref or OpenAlex, it doesn't just store metadata — it understands papers using NLP.

Features you won't find elsewhere:

  • TLDR summaries — one-sentence AI summaries for millions of papers
  • Influence scores — not just citation count, but citation quality
  • Paper recommendations — "papers like this one"
  • Author disambiguation — AI resolves which "J. Smith" is which

Quick Example 

import requests

resp = requests.get("https://api.semanticscholar.org/graph/v1/paper/search", params={
    "query": "large language models reasoning",
    "limit": 3,
    "fields": "title,year,citationCount,tldr"
})

for paper in resp.json()["data"]:
    print(f"\n{paper['title']} ({paper['year']})")
    print(f"  Citations: {paper['citationCount']}")
    if paper.get('tldr'):
        print(f"  TLDR: {paper['tldr']['text']}")
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Sample output:

Chain-of-Thought Prompting Elicits Reasoning (2022)
  Citations: 2847
  TLDR: Chain of thought prompting enables LLMs to solve complex reasoning tasks by generating intermediate steps.

Self-Consistency Improves Chain of Thought Reasoning (2023)
  Citations: 1203
  TLDR: Sampling multiple reasoning paths and taking majority vote significantly improves accuracy.
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That TLDR saved me from reading the abstract. For scanning 100 papers, this is invaluable.

Get Paper Recommendations 

# Find papers similar to "Attention Is All You Need"
paper_id = "204e3073870fae3d05bcbc2f6a8e263d9b72e776"
resp = requests.get(
    f"https://api.semanticscholar.org/recommendations/v1/papers/forpaper/{paper_id}",
    params={"limit": 5, "fields": "title,year,citationCount"}
)

for rec in resp.json()["recommendedPapers"]:
    print(f"{rec['title']} ({rec['year']}) — {rec['citationCount']} citations")
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This is like "Related Papers" on steroids — it uses AI to find semantically similar work, not just papers that cite each other.

Get Author Profiles 

# Yoshua Bengio's profile
resp = requests.get(
    "https://api.semanticscholar.org/graph/v1/author/1741101",
    params={"fields": "name,hIndex,paperCount,citationCount"}
)
author = resp.json()
print(f"{author['name']}")
print(f"  h-index: {author['hIndex']}")
print(f"  Papers: {author['paperCount']}")
print(f"  Citations: {author['citationCount']}")
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Free vs Paid

Semantic Scholar offers:

  • No key: 100 requests per 5 minutes
  • Free key (signup): 1,000 requests per 5 minutes
  • Research partner: unlimited (apply)

For most projects, the no-key tier is plenty.

Academic API Comparison

API Papers TLDR Recommendations Full Text
Semantic Scholar 200M+ ✅ Yes ✅ Yes No
OpenAlex 250M+ No No No
Crossref 150M+ No No No
CORE 300M+ No No ✅ Yes
PubMed 36M+ No Similar articles No

Each has its strength. I use Semantic Scholar for discovery, Crossref for DOI metadata, and CORE for full text.

All toolkits are on my GitHub.

Do you use Semantic Scholar? What's your workflow for finding relevant papers?

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