# Incorporating Structured Commonsense Knowledge in Story Completion

arXiv: https://arxiv.org/abs/1811.00625 · HTML: https://mlsanity.com/paper/1811.00625/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 23 of 23)

- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization](https://mlsanity.com/paper/2110.10834/) (2021)
- [Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://mlsanity.com/paper/2104.08400/) (2021)
- [Summarization as Indirect Supervision for Relation Extraction](https://mlsanity.com/paper/2205.09837/) (2022)
- [Multi-Agent Collaboration via Cross-Team Orchestration](https://mlsanity.com/paper/2406.08979/) (2024)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [TAGPRIME: A Unified Framework for Relational Structure Extraction](https://mlsanity.com/paper/2205.12585/) (2022)
- [Coalescing Global and Local Information for Procedural Text Understanding](https://mlsanity.com/paper/2208.12848/) (2022)
- [Through the Theory of Mind's Eye: Reading Minds with Multimodal Video Large Language Models](https://mlsanity.com/paper/2406.13763/) (2024)
- [C3KG: A Chinese Commonsense Conversation Knowledge Graph](https://mlsanity.com/paper/2204.02549/) (2022)
- [Controllable Text Generation in the Instruction-Tuning Era](https://mlsanity.com/paper/2405.01490/) (2024)
- [A Graph Enhanced BERT Model for Event Prediction](https://mlsanity.com/paper/2205.10822/) (2022)
- [Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs](https://mlsanity.com/paper/2210.10144/) (2022)
- [M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations](https://mlsanity.com/paper/2302.09418/) (2023)
- [DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach](https://mlsanity.com/paper/2410.22631/) (2024)
- [Transferring Procedural Knowledge across Commonsense Tasks](https://mlsanity.com/paper/2304.13867/) (2023)
- [A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks](https://mlsanity.com/paper/2405.10251/) (2024)
- [An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters](https://mlsanity.com/paper/2209.00200/) (2022)
- [A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines](https://mlsanity.com/paper/2212.00298/) (2022)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters](https://mlsanity.com/paper/2210.03264/) (2022)
- [Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion](https://mlsanity.com/paper/2308.11720/) (2023)
- [Enhancing Sentiment Analysis Results through Outlier Detection Optimization](https://mlsanity.com/paper/2311.16185/) (2023)

## Abstract (arXiv)

The ability to select an appropriate story ending is the first step towards perfect narrative comprehension. Story ending prediction requires not only the explicit clues within the context, but also the implicit knowledge (such as commonsense) to construct a reasonable and consistent story. However, most previous approaches do not explicitly use background commonsense knowledge. We present a neural story ending selection model that integrates three types of information: narrative sequence, sentiment evolution and commonsense knowledge. Experiments show that our model outperforms state-of-the-art approaches on a public dataset, ROCStory Cloze Task , and the performance gain from adding the additional commonsense knowledge is significant.