Verified claim · AI-ML · 100% confidence
ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004).
Last verified 2026-05-16 · Methodology veritas-v0.1 · b0eb5c8ac5b4b21e
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Related verified claims
More verified claims related to this one — keep exploring.
BLEU score introduced in paper: BLEU: a Method for Automatic Evaluation of Machine Translation (Papineni et al., 2002).
100% confidence · shares 3 tags (evaluation-metric, foundational, acl)
Byte-Pair Encoding (BPE) for Neural Machine Translation introduced in paper: Neural Machine Translation of Rare Words with Subword Units (Sennrich et al., 2015).
100% confidence · shares 2 tags (foundational, acl)
Byte-Pair Encoding (BPE) for NMT introduced in paper: Neural Machine Translation of Rare Words with Subword Units (Sennrich et al., 2015).
100% confidence · shares 2 tags (foundational, acl)
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017).
100% confidence · shares 1 tag (foundational)
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
100% confidence · shares 1 tag (foundational)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
100% confidence · shares 1 tag (foundational)
Low-Rank Adaptation (LoRA) introduced in paper: LoRA: Low-Rank Adaptation of Large Language Models (Hu et al., 2021).
100% confidence · shares 1 tag (foundational)
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
100% confidence · shares 1 tag (foundational)
Structured fields
- Subject
- ROUGE score
- Predicate
introduced_in_paper- Object
- ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004)
- Confidence
- 100%
- Tags
- rouge · evaluation-metric · summarization · foundational · 2004 · acl
Sources (2)
[1] peer reviewed · ACL Anthology (Lin) · 2004-07-25
ROUGE: A Package for Automatic Evaluation of Summaries“ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans.”
[2] docs · Wikipedia
ROUGE (metric) — WikipediaWikipedia is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004). — SourceScore Claim b0eb5c8ac5b4b21e (verified 2026-05-16). https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the claim record + top cited source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/b0eb5c8ac5b4b21e/" width="100%" height="360" frameborder="0" loading="lazy" title="ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004)."></iframe>Preview: open in new tab
Frequently asked questions
How has SourceScore reviewed the claim "ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004)."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004)."?
The record lists 2 cited source(s): ACL Anthology (Lin), Wikipedia. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
cURL
curl https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "ROUGE score introduced in paper: ROUGE: A Package for Automatic Evaluation of Summaries (Lin, 2004)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_rouge_score_fact() -> dict:
"""Fetch the verified SourceScore claim for ROUGE score."""
r = httpx.get("https://sourcescore.org/api/v1/claims/b0eb5c8ac5b4b21e.json")
return r.json()