# Story Generation from Sequence of Independent Short Descriptions

arXiv: https://arxiv.org/abs/1707.05501 · HTML: https://mlsanity.com/paper/1707.05501/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 26 of 26)

- [COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion](https://mlsanity.com/paper/2106.02497/) (2021)
- [Affective and Dynamic Beam Search for Story Generation](https://mlsanity.com/paper/2310.15079/) (2023)
- [FairyTailor: A Multimodal Generative Framework for Storytelling](https://mlsanity.com/paper/2108.04324/) (2021)
- [CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions](https://mlsanity.com/paper/2210.05221/) (2022)
- [Controlled Cue Generation for Play Scripts](https://mlsanity.com/paper/2112.06953/) (2021)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)
- [InFillmore: Frame-Guided Language Generation with Bidirectional Context](https://mlsanity.com/paper/2103.04941/) (2021)
- [TopNet: Learning from Neural Topic Model to Generate Long Stories](https://mlsanity.com/paper/2112.07259/) (2021)
- [Psychology-guided Controllable Story Generation](https://mlsanity.com/paper/2210.07493/) (2022)
- [Interactive Text Generation](https://mlsanity.com/paper/2303.00908/) (2023)
- [Controlling keywords and their positions in text generation](https://mlsanity.com/paper/2304.09516/) (2023)
- [Modeling Social Readers: Novel Tools for Addressing Reception from Online Book Reviews](https://mlsanity.com/paper/2105.01150/) (2021)
- [LSTM-Based Text Generation: A Study on Historical Datasets](https://mlsanity.com/paper/2403.07087/) (2024)
- [Arabic Automatic Story Generation with Large Language Models](https://mlsanity.com/paper/2407.07551/) (2024)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Collocation2Text: Controllable Text Generation from Guide Phrases in Russian](https://mlsanity.com/paper/2206.09248/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- ["Kurosawa": A Script Writer's Assistant](https://mlsanity.com/paper/2308.03122/) (2023)
- [From Image Captioning to Visual Storytelling](https://mlsanity.com/paper/2508.14045/) (2025)
- [Skeleton-based Coherence Modeling in Narratives](https://mlsanity.com/paper/2604.02451/) (2026)

## Abstract (arXiv)

Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity. Effective solutions or, at least evaluations of modern NLG paradigms for such creative tasks have been elusive, unfortunately. This paper introduces and addresses the task of coherent story generation from independent descriptions, describing a scene or an event. Towards this, we explore along two popular text-generation paradigms -- (1) Statistical Machine Translation (SMT), posing story generation as a translation problem and (2) Deep Learning, posing story generation as a sequence to sequence learning problem. In SMT, we chose two popular methods such as phrase based SMT (PB-SMT) and syntax based SMT (SYNTAX-SMT) to `translate' the incoherent input text into stories. We then implement a deep recurrent neural network (RNN) architecture that encodes sequence of variable length input descriptions to corresponding latent representations and decodes them to produce well formed comprehensive story like summaries. The efficacy of the suggested approaches is demonstrated on a publicly available dataset with the help of popular machine translation and summarization evaluation metrics.