Running summarizing tools on a new article

Abstract: This dataset contains the manual validation of an LLM-based speaker-attribution pipeline that assigns every dialogue line in a film's subtitle track to the character who utters it, developed for research on gendered speaking time in cinema. It covers five films in five languages: C'è ancora domani (Italian, 2023), Furiosa: A Mad Max Saga (English, 2024), Das Lehrerzimmer (German, 2023), Le Comte de Monte-Cristo (French, 2024) and La infiltrada (Spanish, 2024), for a total of 7,415 dialogue lines and about 3 hours 54 minutes of speech. Each film comes as one xlsx file with two sheets. The lines sheet has one row per speaking turn with four core columns: line_id (the cue number in the source subtitle file), timestamp, predicted_speaker (the pipeline's output) and true_speaker (the researcher's manual coding, done character by character against the film); where present, a notes column records validation corrections, i.e. turns found to contain words by a second speaker, split pro-rata by word count, with the corrected fragment quoted. The coding notes sheet documents, for each film, the subtitle source, the unit of analysis, the exclusions (non-dialogue cues), the evidence used for attribution, and the centrality coding: named characters follow Lauzen's major-character criterion (more than one scene and instrumental to the narrative), while all other speakers are pooled into minor character (male) / minor character (female). Character relabels (episodic named characters pooled into the minor buckets under the researcher's supervision) are documented in the coding notes of each film. The subtitle text is not redistributed for copyright reasons: rows link back to the publicly available subtitle tracks (OpenSubtitles ids in the coding notes) via line_id and timestamp. A companion Jupyter notebook reproduces all reported figures: per-film confusion matrices weighted by speaking time and a summary figure with duration-weighted accuracy per film (from 88.0% to 96.5%; pooled: 90.3%), gender attribution (pooled: 96.6%) and the Gini bias of named-character speaking-time concentration (pipeline − manual, within ±0.02 for all films and genders). The dataset supports the reuse of subtitle-based speaker attribution in research on character speech distribution, and provides a benchmark for text-only attribution methods in five languages.