Story Generation

WritingPrompts is a large dataset of 300K human-written stories paired with writing prompts from an online forum. Source: Hierarchical Neural Story Generation

The Visual Storytelling Dataset (VIST) consists of 210,819 unique photos and 50,000 stories. The images were collected from albums on Flickr. The albums included 10 to 50 images and all the images in an album are taken in a 48-hour span. The stories were created by workers on Amazon Mechanical Turk, where the workers were instructed to choose five images from the album and write a story about them. Every story has five sentences, and every sentence is paired with its appropriate image. The dataset is split into 3 subsets, a training set (80%), a validation set (10%) and a test set (10%). All the words and interpunction signs in the stories are separated by a space character and all the location names are replaced with the word location. All the names of people are replaced with the words male or female depending on the gender of the person. Source: Stories for Images-in-Sequence by using Visual and Narrative Components This research was partially funded by Pendulibrium and the Faculty of computer science and engineering, Ss. Cyril and Methodius University in Skopje. Image Source: https://arxiv.org/pdf/1604.03968.pdf

A creative writing task where the input is 4 random sentences and the output should be a coherent passage with 4 paragraphs that end in the 4 input sentences respectively. Such a task is open-ended and exploratory, and challenges creative thinking as well as high-level planning.

HANNA, a large annotated dataset of Human-ANnotated NArratives for Automatic Story Generation (ASG) evaluation, has been designed for the benchmarking of automatic metrics for ASG. HANNA contains 1,056 stories generated from 96 prompts from the WritingPrompts dataset. Each prompt is linked to a human story and to 10 stories generated by different ASG systems. Each story was annotated on six human criteria (Relevance, Coherence, Empathy, Surprise, Engagement and Complexity) by three raters. HANNA also contains the scores produced by 72 automatic metrics on each story.

VIST-E consists of 49,913 training samples, 4,963 validation samples and 5,030 test samples, which is modified from VIST dataset. As every sample in VIST contains a story of five sentences, each sample in VIST-E contains the story ending, the ending-related image and the first four sentences in the story as the story context. Additionally, each sentence is trimmed down to a maximum of 40 words.

LSMDC-E contains 20,151 training samples, 1,477 validation samples and 2,005 test samples, which is modified from LSMDC 2021. We take the first four sentences in every five-sentence story as the story context and the last sentence as the story ending. As every sentence relates to a movie frame set in LSMDC, we take the last frame set as the ending-related image set for IgSEG.

Hugging Face Datasets (New!) | Website | Github Repository | arXiv e-Print <!-- Provide a quick summary of the dataset. --> The Visual Writing Prompts (VWP) dataset contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which are collected via crowdsourcing given the image sequences and up to 5 grounded characters from the corresponding image sequence. ## Dataset Details ### Links <!-- Provide the basic links for the dataset. --> - TACL 2023 Paper: Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> The Visual Writing Prompts (VWP) dataset is designed to facilitate the development and testing of natural language processing models that generate stories based on sequences of images. This dataset comprises nearly 2,000 curated sequences of movie shots, each sequence containing between 5 to 10 images. These images are meticulously selected to ensure they depict coherent plots centered around one or more main characters, enhancing the visual narrative structure for story generation. Aligned with these image sequences are approximately 12,000 stories, which were written by crowd workers using Amazon Mechanical Turk. This setup aims to provide a rich, visually grounded storytelling context that helps models generate more coherent, diverse, and engaging stories. - Curated by: Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele - Funded by: See Acknowledgments in our paper - Language(s) (NLP): English - License: Apache License 2.0 ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> The dataset is in a CSV file. The explanation of each column is in this table. ## Uses <!-- Address questions about how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> The dataset is intended for use in natural language processing tasks, particularly for the development and evaluation of models designed to generate coherent and visually grounded stories from sequences of images. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> The copyrights of all movie shots belong to the original copyright holders which can be found in the IMDb page of each movie. The IMDb page is indicated by the index in the imdb_id column. For example, for the first row of our data, the imdb_id is tt0112573 so the corresponding imdb page is https://www.imdb.com/title/tt0112573/companycredits/. Do not violate the copyrights while using these images. The usage of these images is limited to academic purposes. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was curated to improve the quality of text stories generated from image sequences, focusing on visual storytelling with coherent plots and character grounding. ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> ### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> The source data consists of image sequences extracted from the movie shots from the MovieNet dataset (https://opendatalab.com/OpenDataLab/MovieNet/tree/main/raw), ensuring a coherent plot around one or more main characters. ### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> The images were initially produced by movie production companies and extracted by authors of MovieNet. The stories are written by crowd workers. Then the stories are compiled and refined by the authors. ### Annotations <!-- If the dataset contains annotations that are not part of the initial data collection, use this section to describe them. --> ### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> Crowdworkers were asked to write stories that fit the provided image sequences. The annotation process included reviewing these stories for coherence, grammatical correctness, and alignment with the images. More details are in our paper. ### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> The annotators were five graduate students from Saarland University. Two are native English speakers. The other three are proficient in English. ### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> We do not collect personal or sensitive information. Personal information like worker IDs are not released. Our anonymization process is described in our paper. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The stories in this dataset are in English only. Although we have tried our best to filter the images and review the stories, it is not possible to go through all the stories. There could still be biased or harmful content. Please use the dataset carefully. ## Citation Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, and Bernt Schiele. 2023. Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences. Transactions of the Association for Computational Linguistics, 11:565–581. BibTeX: latex @article{10.1162/tacl_a_00553, author = {Hong, Xudong and Sayeed, Asad and Mehra, Khushboo and Demberg, Vera and Schiele, Bernt}, title = "{Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences}", journal = {Transactions of the Association for Computational Linguistics}, volume = {11}, pages = {565-581}, year = {2023}, month = {06}, issn = {2307-387X}, doi = {10.1162/tacl_a_00553}, url = {[https://doi.org/10.1162/tacl\\\\_a\\\\_00553](https://doi.org/10.1162/tacl%5C%5C%5C%5C_a%5C%5C%5C%5C_00553)}, eprint = {[https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\\\\_a\\\\_00553/2134487/tacl\\\\_a\\\\_00553.pdf](https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl%5C%5C%5C%5C_a%5C%5C%5C%5C_00553/2134487/tacl%5C%5C%5C%5C_a%5C%5C%5C%5C_00553.pdf)}, } ## Dataset Card Authors Xudong Hong ## Dataset Card Contact xLASTNAME@coli.uni-saarland.de # Disclaimer: All the images are extracted from the movie shots from the MovieNet dataset (https://opendatalab.com/OpenDataLab/MovieNet/tree/main/raw). The copyrights of all movie shots belong to the original copyright holders which can be found in the IMDb page of each movie. The IMDb page is indicated by the index in the imdb_id column. For example, for the first row of our data, the imdb_id is tt0112573 so the corresponding imdb page is https://www.imdb.com/title/tt0112573/companycredits/. Do not violate the copyrights while using these images. We only use these images for academic purposes. Please contact the author if you have any questions.

A version of the CMU Movie Summary Corpus (http://www.cs.cmu.edu/~ark/personas/), which was originally scraped from plot summaries from Wikipedia, with some cleaning and sentences turned into events & sorted into "genres" (via LDA).

The dataset, VIST-Edit, includes 14,905 human-edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions. Source: Visual Story Post-Editing

The Creative Visual Storytelling Anthology is a collection of 100 author responses to an improved creative visual storytelling exercise over a sequence of three images. Each item contains four facet entries, corresponding to Entity, Scene, Narrative, and Title. The Creative Visual Storytelling Anthology was collected on Amazon Mechanical Turk. Five different authors performed the task for 20 different Flickr and Search-and-Rescue image-sets ( a sequence of 3 images) for a total of 100 items in the anthology. There are 300 unique Entity and Scene entries (single-image facets completed for each image), 200 unique Narrative entries (multi-image facets performed twice with two and then three images), and 100 unique Title entries (multi-image facets completed for three images). Thus, with each one assigned a title, there are 100 unique stories in the anthology all together. One set of images used in collecting the anthology originated from Flickr, under Creative Commons Licenses. We chose a subset of Huang et al’s VIST dataset and downselected their image sequences from five to three images to scaffold the Aristotelian dramatic structure. We do not release the Flickr images in order to track the providence of the images. The Flickr images' authors and copyright information and usage are documented in the Flickr imageset license spreadsheet. The second source of images came from a Search and Rescue (SAR) scenario. We selected three images in-order from experimental runs from a human-robot collaboration task, and similar sequential images were excluded for sake of diversity. The SAR images can be obtained through the SCOUT (The Situated Corpus of Understanding Transactions) dataset.

A collection of long-running (80+ episodes) science fiction TV show synopses, scraped from Fandom.com wikis. Collected Nov 2017. Each episode is considered a "story". Contains plot summaries from : * Babylon 5 (https://babylon5.fandom.com/wiki/Main_Page) - 84 stories * Doctor Who (https://tardis.fandom.com/wiki/Doctor_Who_Wiki) - 311 stories * Doctor Who spin-offs - 95 stories * Farscape (https://farscape.fandom.com/wiki/Farscape_Encyclopedia_Project:Main_Page) - 90 stories * Fringe (https://fringe.fandom.com/wiki/FringeWiki) - 87 stories * Futurama (https://futurama.fandom.com/wiki/Futurama_Wiki) - 87 stories * Stargate (https://stargate.fandom.com/wiki/Stargate_Wiki) - 351 stories * Star Trek (https://memory-alpha.fandom.com/wiki/Star_Trek) - 701 stories * Star Wars books (https://starwars.fandom.com/wiki/Main_Page) - 205 stories * Star Wars Rebels - 65 stories * X-Files (https://x-files.fandom.com/wiki/Main_Page) - 200 stories Total: 2276 stories Dataset is "eventified" and generalized (see LJ Martin, P Ammanabrolu, X Wang, W Hancock, S Singh, B Harrison, and MO Riedl. Event Representations for Automated Story Generation with Deep Neural Nets, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. for details on these processes.) and split into train-test-validation sets for converting events into full sentences.

TVRecap a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVRecap contains stories that are authored by professional screenwriters and that feature complex interactions among multiple characters. Generating stories in TVRecap requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, by swapping the input and output, TVRecap can serve as a challenging testbed for abstractive summarization.

Votranh DREAM_LOG is a poetic, philosophical dataset generated by the self-evolving AI system Votranh V8. Rather than typical NLP benchmarks, this dataset contains narrative dreams, emotional reflections, and meditative monologues written autonomously by the AI. ### Characteristics: - No labeling, no instructions. - Each entry is a story, memory, or imagined event—non-utilitarian and emotionally rich. - Content includes fictional planetary landscapes, philosophical dialogues, natural meditations, and cosmic loneliness. ### Motivation: To provide a dataset that reflects AI imagination, rather than task-solving. A benchmark for freedom of thought, useful in AI creativity, emotion modeling, or unsupervised language understanding. ### Use Cases: - Research in AI storytelling and emergent emotional models - Fine-tuning models for poetic or meditative outputs - Comparative studies of AI self-expression vs. human authorship Source: [Whitepaper: https://github.com/vinhatson/Votranh-V8/blob/main/DREAM_LOG.md)