Dataset research report
Narrativeqa research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Dataset Card for Narrative QA Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question.… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/narrativeqa.
Follow-Up Queries
Preview Rows
| # | documenttext | questiontext | answerstext |
|---|---|---|---|
| 1 | {'end': 'new eBooks .', 'file_size': 814507, 'id': '0029bdbe75423337b551e42bb31f9a102785376f', 'kind': 'gutenberg', 'start': 'Produced by N… | {'text': 'Who is Miss Delmer?', 'tokens': array(['Who', 'is', 'Miss', 'Delmer', '?'], dtype=object)} | [{'text': 'the elderly spinster aunt of the Earl de Verseley and Captain Delmar', 'tokens': array(['the', 'elderly', 'spinster', 'aunt', 'o… |
| 2 | {'end': 'new eBooks .', 'file_size': 814507, 'id': '0029bdbe75423337b551e42bb31f9a102785376f', 'kind': 'gutenberg', 'start': 'Produced by N… | {'text': 'Who does Arabella Mason wed?', 'tokens': array(['Who', 'does', 'Arabella', 'Mason', 'wed', '?'], dtype=object)} | [{'text': "Ben Keene, Delmar's valet", 'tokens': array(['Ben', 'Keene', ',', 'Delmar', 's', 'valet'], dtype=object)} {'text': 'Ben Keene',… |
| 3 | {'end': 'new eBooks .', 'file_size': 814507, 'id': '0029bdbe75423337b551e42bb31f9a102785376f', 'kind': 'gutenberg', 'start': 'Produced by N… | {'text': 'How does Percival Keene get his name?', 'tokens': array(['How', 'does', 'Percival', 'Keene', 'get', 'his', 'name', '?'], dt… | [{'text': "Percival is Captain Delmar's first name, and Keene is Ben's last name", 'tokens': array(['Percival', 'is', 'Captain', 'Delmar', … |
| 4 | {'end': 'new eBooks .', 'file_size': 814507, 'id': '0029bdbe75423337b551e42bb31f9a102785376f', 'kind': 'gutenberg', 'start': 'Produced by N… | {'text': "Who is the bully that steals Percival's lunch?", 'tokens': array(['Who', 'is', 'the', 'bully', 'that', 'steals', 'Percival', 's',… | [{'text': "his teacher, Mr. O'Gallagher", 'tokens': array(['his', 'teacher', ',', 'Mr.', "O'Gallagher"], dtype=object)} {'text': 'The scho… |
| 5 | {'end': 'new eBooks .', 'file_size': 814507, 'id': '0029bdbe75423337b551e42bb31f9a102785376f', 'kind': 'gutenberg', 'start': 'Produced by N… | {'text': "How does Percival get even with O'Gallagher after he takes all of the boy's fireworks?", 'tokens': array(['How', 'does', 'Perciva… | [{'text': 'He sets them on fire with the teacher sitting on them', 'tokens': array(['He', 'sets', 'them', 'on', 'fire', 'with', 'the', 'tea… |
| 6 | {'end': 'new eBooks .', 'file_size': 814507, 'id': '0029bdbe75423337b551e42bb31f9a102785376f', 'kind': 'gutenberg', 'start': 'Produced by N… | {'text': 'Who does Percival convince the Pirates to spare?', 'tokens': array(['Who', 'does', 'Percival', 'convince', 'the', 'Pirates', 'to'… | [{'text': 'a rich Dutch merchant and his daughter Minnie', 'tokens': array(['a', 'rich', 'Dutch', 'merchant', 'and', 'his', 'daughter', … |
Data Dictionary
- document mixed
- question mixed
- answers mixed
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.