Data-to-Text Generation with Content Selection and Planning (1809.00582v2)

Abstract: Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model outperforms strong baselines improving the state-of-the-art on the recently released RotoWire dataset.

Analysis of Data-to-Text Generation with Content Selection and Planning

The paper "Data-to-Text Generation with Content Selection and Planning" by Puduppully, Dong, and Lapata, introduces a novel approach to improving data-to-text generation systems. The model presented focuses on efficiently producing coherent and accurate text from structured datasets, specifically addressing the challenges associated with content selection, planning, and realizing document structure.

The approach introduced in this paper involves a hybrid neural network architecture that maintains end-to-end differentiability while incorporating explicit planning stages. The authors decompose the generation task into two clear steps: content selection and content planning, followed by text generation. This decomposition is quite significant as it tackles existing issues in neural generation models, such as maintaining factual correctness and clarity in long-form document generation.

Methodology

The methodology involves two primary components:

1. Content Selection and Planning: This stage comprises a gated mechanism that leverages the interdependencies between records in a dataset to select relevant content. Utilizing a pointer network, it then forms a content plan, which sequentially arranges the chosen pieces of information based on a trained content model.
2. Text Generation: In the subsequent step, the model generates text informed by the established content plan. This phase integrates a sequence decoder augmented with a copy mechanism, improving the factual accuracy of the generated text by allowing for direct referral back to entities in the source data.

The authors evaluate their system using the RotoWire dataset, which contains data from NBA games paired with game summaries. The use of the RotoWire dataset highlights the effectiveness of the model in managing complex datasets with a high dimensionality of input records.

Results and Implications

The evaluation results indicate that the proposed model achieves substantial improvements in content selection, planning accuracy, and document coherence over previous methods, such as template-based and traditional encoder-decoder approaches. Specifically, the system demonstrated a marked increase in relevant fact generation as well as improved precision and recall of content selection metrics.

Several advantages emerge from explicitly modeling content plans. Firstly, explicit content planning helps to organize document structure at a high level, simplifying downstream tasks for the decoder. Furthermore, it enhances interpretability due to the intermediary content representation, and crucially, reduces redundancy, limiting the potential for repetitive or extraneous information in the output text.

Practical and Theoretical Implications

From a practical standpoint, this work presents implications for any applications necessitating structured data verbalization, such as automated reporting in journalism, personalized summaries in customer service platforms, and narrative generation in digital storytelling. In theory, the explicit content planning paradigm could stimulate further research on improving the architecture of neural network-based generative models for tasks involving structured information.

Future Directions

The paper opens several avenues for future work. Enhancements in learning more complex or hierarchical content plans could provide richer, more nuanced summaries. Cross-domain validation would also be beneficial in assessing the generalizability and stability of this approach when applied to datasets with different linguistic or structural characteristics.

In conclusion, Puduppully, Dong, and Lapata’s work offers a well-structured method for improving data-to-text generation, showing substantial potential in both its current form and its future development. The explicit modeling of content selection and planning emerges as a promising direction for advancing the effectiveness and reliability of automatic text generation systems.