{"url":"/dataset/mensa","name":"MENSA","full_name":"Movie Scene Saliency Dataset","description_markdown":"# MENSA: Movie Scene Saliency Dataset\r\n\r\n## Dataset Summary\r\n\r\nThe dataset, MENSA (Movie Scene Saliency Dataset) is from the paper \"**Select and Summarize: Scene Saliency for Movie Script Summarization**\", and consists of movie scripts and their corresponding summaries. Each scene in the movie script is annotated with scene saliency labels. The training set contains silver labels, which are automatically generated, while the validation and test sets contain human-annotated gold labels.\r\n\r\n## Dataset Structure\r\n\r\nThe dataset is divided into three parts:\r\n- **Training Set**: Contains movie scripts and summaries with silver scene saliency labels.\r\n- **Validation Set**: Contains movie scripts and summaries with human-annotated gold scene saliency labels.\r\n- **Test Set**: Contains movie scripts and summaries with human-annotated gold scene saliency labels.","description_withheld":null,"homepage":"","introduced_date":"2024-04-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/select-and-summarize-scene-saliency-for-movie","title":"Select and Summarize: Scene Saliency for Movie Script Summarization","first_author":"Rohit Saxena","url":null},"license":null,"modalities":[],"tasks":[{"name":"Long-Form Narrative Summarization","url":"/task/long-form-narrative-summarization","datasets_with_task":"/datasets/task/long-form-narrative-summarization"}],"languages":[],"variants":["MENSA"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/long-form-narrative-summarization-on-mensa","task":"Long-Form Narrative Summarization","dataset_variant":"MENSA","rows":10,"metrics":["BERTScore (F1)","ROUGE-1","ROUGE-2","ROUGE-L"],"first_row_in_archive_order":{"model":"NexusSum (Mistral Large)","paper":"/paper/nexussum-hierarchical-llm-agents-for-long","metrics":{"BERTScore (F1)":"65.73","ROUGE-1":"44.91","ROUGE-2":"11.43","ROUGE-L":"19.23"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/nexussum-hierarchical-llm-agents-for-long","title":"NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization","date":"2025-05-30","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/agent-as-judge-for-factual-summarization-of","title":"Agent-as-Judge for Factual Summarization of Long Narratives","date":"2025-01-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/end-to-end-long-document-summarization-using","title":"End-to-End Long Document Summarization using Gradient Caching","date":"2025-01-03","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/select-and-summarize-scene-saliency-for-movie","title":"Select and Summarize: Scene Saliency for Movie Script Summarization","date":"2024-04-04","rows_on_this_dataset":3,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}