{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/rouge-results-to-str","entry":"rouge_results_to_str","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":10,"n_papers_ran":6,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":4,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":10,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":3},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2405.04163","paper":"/paper/medvoc-vocabulary-adaptation-for-fine-tuning","title":"MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization","date":"2024-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gb-kgp/MEDVOC","path":"src/Model_Scripts/BertSumAbs_Modified_Scripts/src/cal_rouge.py","file_url":"https://github.com/gb-kgp/MEDVOC/blob/HEAD/src/Model_Scripts/BertSumAbs_Modified_Scripts/src/cal_rouge.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"14b39c4dc7a42e40","mcp_get_code":{"code_sha256":"14b39c4dc7a42e40"}},{"arxiv_id":"2212.10218","paper":"/paper/ganlm-encoder-decoder-pre-training-with-an","title":"GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator","date":"2022-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csjianyang/ganlm","path":"evaluation/cnn_dm.py","file_url":"https://github.com/csjianyang/ganlm/blob/HEAD/evaluation/cnn_dm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c19b75e22d2023fb","mcp_get_code":{"code_sha256":"c19b75e22d2023fb"}},{"arxiv_id":"2207.03509","paper":"/paper/meta-learning-the-difference-preparing-large","title":"Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation","date":"2022-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-research/meta-learning-the-difference","path":"abstractive_summarization/src/cal_rouge.py","file_url":"https://github.com/amazon-research/meta-learning-the-difference/blob/HEAD/abstractive_summarization/src/cal_rouge.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3aa756a41c020da7","mcp_get_code":{"code_sha256":"3aa756a41c020da7"}},{"arxiv_id":"2004.14135","paper":"/paper/bert-fine-tuning-for-arabic-text","title":"BERT Fine-tuning For Arabic Text Summarization","date":"2020-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mukhtar-algezoli/Arabic_PreSumm","path":"src/cal_rouge.py","file_url":"https://github.com/mukhtar-algezoli/Arabic_PreSumm/blob/HEAD/src/cal_rouge.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14b39c4dc7a42e40","mcp_get_code":{"code_sha256":"14b39c4dc7a42e40"}},{"arxiv_id":"1910.14142","paper":"/paper/discourse-aware-neural-extractive-model-for","title":"Discourse-Aware Neural Extractive Text Summarization","date":"2019-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiacheng-xu/DiscoBERT","path":"model/pyrouge_metrics.py","file_url":"https://github.com/jiacheng-xu/DiscoBERT/blob/HEAD/model/pyrouge_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14b39c4dc7a42e40","mcp_get_code":{"code_sha256":"14b39c4dc7a42e40"}},{"arxiv_id":"1908.08345","paper":"/paper/text-summarization-with-pretrained-encoders","title":"Text Summarization with Pretrained Encoders","date":"2019-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nachotp/BertCommentSum","path":"src/cal_rouge.py","file_url":"https://github.com/nachotp/BertCommentSum/blob/HEAD/src/cal_rouge.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14b39c4dc7a42e40","mcp_get_code":{"code_sha256":"14b39c4dc7a42e40"}},{"arxiv_id":"1905.13164","paper":"/paper/hierarchical-transformers-for-multi-document","title":"Hierarchical Transformers for Multi-Document Summarization","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nlpyang/hiersumm","path":"src/abstractive/cal_rouge.py","file_url":"https://github.com/nlpyang/hiersumm/blob/HEAD/src/abstractive/cal_rouge.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"14b39c4dc7a42e40","mcp_get_code":{"code_sha256":"14b39c4dc7a42e40"}},{"arxiv_id":"1903.10318","paper":"/paper/fine-tune-bert-for-extractive-summarization","title":"Fine-tune BERT for Extractive Summarization","date":"2019-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TidalPaladin/neural-summarizer","path":"src/others/utils.py","file_url":"https://github.com/TidalPaladin/neural-summarizer/blob/HEAD/src/others/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7e979303d1cfbf18","mcp_get_code":{"code_sha256":"7e979303d1cfbf18"}},{"arxiv_id":"aaai_21432","paper":null,"title":"arXiv:aaai_21432","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"microsoft/DialogLM","path":"DialogLM_UniLM/DialogLM/evaluations/eval_for_cnndm.py","file_url":"https://github.com/microsoft/DialogLM/blob/HEAD/DialogLM_UniLM/DialogLM/evaluations/eval_for_cnndm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c19b75e22d2023fb","mcp_get_code":{"code_sha256":"c19b75e22d2023fb"}},{"arxiv_id":"aaai_16089","paper":null,"title":"arXiv:aaai_16089","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nlpyang/presumm","path":"src/cal_rouge.py","file_url":"https://github.com/nlpyang/presumm/blob/HEAD/src/cal_rouge.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14b39c4dc7a42e40","mcp_get_code":{"code_sha256":"14b39c4dc7a42e40"}}]}