{"adoption": {"forks": 410, "observed_at": "2026-08-28T04:04:55.060033+00:00", "stars": 1506}, "canonical_url": "https://ross.abutalabs.com/products/bertsum", "card": {"archived": false, "artifact_type": "library", "description": "Code for paper Fine-tune BERT for Extractive Summarization", "domain": ["deep-learning", "machine-learning"], "enriched": true, "function": ["machine-learning", "deep-learning", "nlp", "llm-training"], "health_score": 20, "homepage": null, "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["nlpyang/BertSum"], "name": "nlpyang/BertSum", "platform": ["python"], "pushed_at": "2022-01-11T07:58:23+00:00", "repo": "nlpyang/BertSum", "stars": 1506, "tags": ["extractive-summarization", "bert", "pytorch", "research-code", "text-summarization", "natural-language-processing", "linux"], "topics": [], "urls": [], "use_cases": ["fine-tune BERT for extractive text summarization", "reproduce ROUGE benchmark results on CNN/DailyMail", "train a sentence-level summarization classifier", "preprocess news articles for summarization research", "compare summarization model architectures like Transformer and LSTM heads"], "what_it_is": "BertSum is the official PyTorch implementation of the paper 'Fine-tune BERT for Extractive Summarization', providing preprocessing pipelines and training code for BERT-based extractive summarization models. It includes data preparation for CNN/DailyMail, multiple summarization head architectures (classifier, Transformer, LSTM), and pretrained model results.", "when_to_avoid": ["you need abstractive summarization rather than extractive", "you want a production-ready, actively maintained library with modern dependency versions", "you need support for newer transformer models beyond BERT or easy installation"], "when_to_choose": ["you need a research-grade extractive summarization baseline with published ROUGE scores", "you want to fine-tune BERT specifically for sentence selection in summarization", "you are working with the CNN/DailyMail dataset and need preprocessing tooling"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/bertsum", "repo": "nlpyang/BertSum", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:55.060033+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T04:32:40.493328+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "e5f362c9299bf7c6a9de72bc36faaf5fed51131e522f790cc932b8cc19bc902e", "fetched_at": "2026-08-28T04:04:55.060033+00:00", "kind": "readme", "missing": false, "url": "https://github.com/nlpyang/BertSum"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2719, "days_push": 1695, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}