Generating Informative Summary Tables from Live Sports Commentary Using Global Tuple Extraction

Core Concepts

Existing text-to-table generation approaches often directly replicate information from the input text, limiting their applicability in real-world scenarios that require information extraction, reasoning, and integration. This work introduces a novel benchmark dataset, LIVESUM, and a robust pipeline called T3 to address these challenges.

Abstract

The paper introduces a new benchmark dataset called LIVESUM for evaluating the information integration capabilities of models in text-to-table generation tasks. LIVESUM consists of 3,771 pairs of live sports commentary texts and corresponding summary tables, focusing on eight types of events in football matches.
The authors propose a three-stage pipeline called T3 (Text-Tuple-Table) to address the task. The pipeline first extracts relevant tuples from the input text using language models, then integrates the tuples, and finally generates one or more summary tables.
The authors benchmark the performance of various state-of-the-art language models on the LIVESUM dataset in both fine-tuning and zero-shot settings. The results show that current language models struggle with information integration, even after fine-tuning, highlighting the challenges of the task.
The authors further demonstrate that the proposed T3 pipeline can significantly improve the performance of language models in the zero-shot setting, outperforming previous approaches on several other text-to-table datasets. The pipeline exhibits strong generalization abilities, showcasing its effectiveness in addressing the information integration challenges in text-to-table generation tasks.

Stats

Player5 scores with a shot from close range to the bottom left corner, assisted by Player12.
Player2 from the Home Team misses a header to the left from the center of the box.
Goal for the Home Team, they lead 1-0 against the Away Team!

Quotes

"Reading extensive texts is demanding and time-consuming for humans, further compounded by the challenge of effectively capturing the key elements."
"Previous studies on text-to-table generation primarily rely on datasets traditionally used for table-to-text tasks, which focus merely on format transformation, where the information in the table and the corresponding text representation are essentially similar."
"To resolve the aforementioned research gaps, we introduce a novel benchmark, LIVESUM, which consists of 3,771 text-based live commentaries from real-world football matches, intending to evaluate the models' ability to generate summary tables."

Key Insights Distilled From

Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction

by Zheye Deng,C... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14215.pdf

Deeper Inquiries

How can the T3 pipeline be extended to handle more complex table structures, such as those with merged cells or hierarchical headers?

To handle more complex table structures like merged cells or hierarchical headers, the T3 pipeline can be extended by incorporating additional stages in the pipeline.

Preprocessing for Merged Cells: Before extracting tuples, the pipeline can include a preprocessing step to identify and handle merged cells. This can involve splitting the merged cells into individual cells to ensure accurate tuple extraction.
Enhanced Tuple Extraction: Modify the tuple extraction stage to handle merged cells by considering the context of the merged cells and extracting information accordingly.
Header Hierarchies: Introduce a step to identify hierarchical headers in the text and map them to the corresponding rows or columns in the table. This can involve a more sophisticated tuple extraction process that captures the hierarchical relationships between headers.
Integration for Complex Structures: Develop advanced integration techniques that can handle the complexities of merged cells and hierarchical headers. This may involve creating rules or algorithms to merge information from multiple cells into a single cell in the table.

What other types of information integration challenges could be incorporated into the LIVESUM dataset to further stress-test the capabilities of language models?

To further stress-test the capabilities of language models, the LIVESUM dataset can incorporate the following information integration challenges:

Temporal Dependencies: Include scenarios where events in the text are described out of order, requiring the model to infer temporal dependencies to construct the table accurately.
Ambiguity Resolution: Introduce ambiguous descriptions in the text that can be interpreted in multiple ways, challenging the model to disambiguate and integrate the correct information.
Inconsistent References: Include instances where the same entity or event is referred to differently in the text, testing the model's ability to resolve references and integrate information cohesively.
Missing Information: Introduce cases where crucial information is missing in the text, requiring the model to infer and integrate the missing data accurately.
Complex Relationships: Include complex relationships between entities or events in the text that need to be captured and integrated into the table, testing the model's reasoning and integration capabilities.

How can the T3 pipeline be adapted to handle text-to-table generation tasks in other domains, such as scientific literature or financial reports, where the information integration requirements may differ?

Adapting the T3 pipeline for text-to-table generation tasks in other domains with different information integration requirements involves the following modifications:

Domain-Specific Tuple Extraction: Customize the tuple extraction stage to identify domain-specific entities, attributes, and relationships relevant to scientific literature or financial reports.
Specialized Integration Rules: Develop domain-specific integration rules or algorithms to handle the unique information integration requirements of scientific literature or financial reports, such as complex formulas or citation structures.
Contextual Understanding: Enhance the pipeline to incorporate domain-specific contextual understanding, such as scientific terminology or financial jargon, to improve the accuracy of tuple extraction and integration.
Data Preprocessing: Implement preprocessing steps tailored to the domain data, such as handling mathematical equations in scientific literature or financial calculations in reports, to ensure accurate table generation.
Evaluation Metrics: Adjust the evaluation metrics to align with the specific requirements of the domain, focusing on metrics that reflect the quality and relevance of the generated tables in scientific or financial contexts.