Generating Narratives with Related Start and Stop Sentences to Achieve Narrative Closure

Core Concepts

Narratives with related start and stop sentences provide a greater sense of closure and coherence compared to narratives with unrelated endpoints.

Abstract

The paper proposes RENARGEN, a framework for generating narratives with related start and stop sentences. This is motivated by the observation that human writers often bookend their writing with related beginning and ending sentences to compose a satisfying narrative.
The key components of RENARGEN are:

Endpoint Generator:

For language models (LMs), it generates a related stop sentence given the start sentence by using a Phrase Generator to extract salient words/phrases from the start, and a Stop Generator to generate the stop incorporating the phrase list.
For large language models (LLMs), it uses various methods to generate a related stop, such as prompting for semantic relatedness, erotetic closure, "matching ending", or entailment.

Story Infiller:

For LMs, it uses an interactive infilling approach that considers both left and right contexts to generate the middle sentences, rather than a simple left-to-right generation.
For LLMs, it generates all the middle sentences at once given the start and stop.

The authors conduct automatic and human evaluations to show that RENARGEN generates narratives with more related endpoints and better overall coherence and closure compared to baseline models.

Stats

"Human writers often bookend their writing with ending sentences that relate back to the beginning sentences in order to compose a satisfying narrative that "closes the loop.""
"Narrative closure is an important feature of satisfying narratives. Carroll (2007) defines narrative closure as "the phenomenological feeling of finality that is generated when all the questions saliently posed by the narrative are answered.""
"Automatic story generation has advanced significantly recently, but these approaches still struggle to generate satisfying and coherent stories with closure."

Quotes

"Narratives with related start and stop sentences provide a greater sense of closure and coherence compared to narratives with unrelated endpoints."
"Endpoint relatedness may be operationalized with various methods, the most common of which is semantic relatedness."
"Through piece-wise narrative generation, RENARGEN offers user interactivity. For example, for LMs the user can control the generated stop sentence by editing the phrase list."

Key Insights Distilled From

Returning to the Start

by Anneliese Br... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00829.pdf

Deeper Inquiries

How can RENARGEN be extended to generate narratives in languages other than English?

To extend RENARGEN to generate narratives in languages other than English, several steps can be taken:

Multilingual Training Data: Incorporate multilingual training data to fine-tune language models on narratives in different languages. This will help the models understand the nuances and structures of various languages.

Language-specific Preprocessing: Implement language-specific preprocessing steps to handle tokenization, sentence segmentation, and other language-specific tasks.

Language-specific Prompting: Develop language-specific prompts for generating related endpoints and infilling stories. This will ensure that the generated narratives maintain cultural and linguistic relevance.

Evaluation in Multiple Languages: Validate the performance of RENARGEN in different languages through automatic and human evaluations to ensure the quality and coherence of the generated narratives.

Collaboration with Linguists: Collaborate with linguists and native speakers of different languages to fine-tune the models and ensure that the generated narratives are culturally appropriate and linguistically accurate.

What are the potential biases in the training data that could be reflected in the generated narratives, and how can they be mitigated?

Potential biases in the training data that could be reflected in the generated narratives include:

Gender Bias: If the training data contains gender stereotypes or biases, the generated narratives may perpetuate these biases. Mitigation strategies include bias detection algorithms, diverse training data, and bias-aware training objectives.

Cultural Bias: Training data that reflects specific cultural norms or stereotypes may lead to biased narratives. To mitigate this, diverse cultural representations in the training data and bias detection mechanisms can be employed.

Socioeconomic Bias: Narratives influenced by socioeconomic biases in the training data can be addressed by ensuring economic diversity in the training samples and incorporating fairness metrics during model training.

Ethical Bias: Biases related to ethical considerations can be mitigated by incorporating ethical guidelines into the training process and evaluating narratives for ethical implications.

Mitigation strategies include:

Diverse Training Data: Ensure the training data is diverse and representative of different demographics and perspectives.
Bias Detection Algorithms: Implement algorithms to detect and mitigate biases in the training data and generated narratives.
Fairness Metrics: Incorporate fairness metrics during model training to identify and address biases.
Ethical Guidelines: Establish clear ethical guidelines for narrative generation and ensure adherence to ethical standards.

How can the concept of narrative closure be further refined and operationalized to capture more nuanced aspects of storytelling?

To refine and operationalize the concept of narrative closure for more nuanced storytelling, the following approaches can be considered:

Emotional Closure: Incorporate emotional resolution and character development to provide a deeper sense of closure for the reader. Ensure that the emotional arcs of characters are satisfactorily concluded.

Theme Closure: Focus on resolving thematic elements introduced in the narrative to create a cohesive and meaningful story. Themes should be revisited and tied up in the conclusion for a sense of completeness.

Symbolic Closure: Utilize symbolic elements throughout the narrative and ensure that these symbols are resolved or interpreted in the conclusion to enhance the narrative closure.

Structural Closure: Experiment with different narrative structures and closure techniques to create varied and engaging storytelling experiences. This could include circular narratives, nested stories, or non-linear storytelling approaches.

Reader Engagement: Consider the reader's perspective and engagement with the narrative closure. Allow for interpretation and reflection to create a more interactive and immersive storytelling experience.

By incorporating these nuanced aspects of storytelling into the concept of narrative closure, RENARGEN can generate more compelling and satisfying narratives that resonate with readers on a deeper level.