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| Experiments show Proposed Method Significantly Enhances Accuracy |
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models
The conventional use of the Retrieval-Augmented Generation (RAG) architecture has proven effective for retrieving information from diverse documents. However, challenges arise in handling complex table queries, especially within PDF documents containing intricate tabular structures.
This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems. Our methodology involves storing PDFs in the retrieval database and extracting tabular content separately. The extracted tables undergo a process of context enrichment, concatenating headers with corresponding values.
To ensure a comprehensive understanding of the enriched data, we employ a fine-tuned version of the Llama-2-chat language model for summarisation within the RAG architecture. Furthermore, we augment the tabular data with contextual sense using the ChatGPT 3.5 API through a one-shot prompt.
This enriched data is then fed into the retrieval database alongside other PDFs. Our approach aims to significantly improve the precision of complex table queries, offering a promising solution to a longstanding challenge in information retrieval.
Submission history
From: Vishesh Tripathi [view email][v1] Thu, 4 Jan 2024 16:16:14 UTC (563 KB)
Summary
- Store PDF documents in the retrieval database to retain full original data.
- Extract tabular data from PDFs separately using Camelot library.
- Enrich extracted tables by concatenating headers with their corresponding row values to provide context.
- Integrate fine-tuned Llama-2-chat language model in RAG architecture for summarization.
- Further augment tabular data with context using ChatGPT 3.5 API and one-shot prompting.
- Add enriched tabular data to retrieval database with original PDFs.
The methodology aims to significantly enhance the precision of complex table queries, which remains a persistent challenge in document-based information retrieval. Experiments demonstrate substantial gains in accuracy, especially for table-related queries, when using the proposed techniques. The integration of advanced language models like Llama-2 and ChatGPT 3.5 is key in imbuing nuanced understanding of enriched tabular content. Overall, the approach marks a promising innovation to overcome limitations in handling intricate tabular structures within documents.
Three methodologies were evaluated for information retrieval accuracy, including the normal existing pipeline, a strategy involving separate table extraction and context enrichment, and the parsing of enriched extracted text through Chat-GPT 3.5 Turbo. The results indicate notable improvements in accuracy metrics, particularly in handling complex table queries. Parsing enriched text with Chat-GPT 3.5 Turbo demonstrates a significant leap, achieving an overall accuracy of 65%, marking a substantial advancement in addressing the challenges of complex table structure.
Authors: uday, biddwan, vishesh.tripathi}@yellow.ai
- All authors are affiliated with Yellow.ai, an AI startup focused on conversational AI.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is an artificial intelligence system architecture that combines large neural network language models with retrieval of external knowledge to improve performance on various natural language processing tasks.
Some key features of RAG systems:
- Uses a dual-encoder framework with a retriever module and a generator module.
- Retriever searches through large document collections to find relevant context for a given query or task. This provides external knowledge to augment the capabilities of the generator.
- Generator is a pre-trained language model like GPT-3 that can leverage the retrieved documents to produce high-quality output.
- Allows blending external knowledge into language model predictions in a dynamic way based on the specific query/task.
- Improves performance over language models alone on tasks like question answering, summarization, dialogue, etc.
- Allows training the system in an end-to-end fashion to optimize retrieval and generation jointly.
So in summary, RAG systems augment language models with on-the-fly access to external knowledge, enhancing their reasoning and inference capabilities compared to using the language model alone. This makes them very promising for knowledge-intensive NLP applications.
Camelot Python Library
Camelot is an open-source Python library that can extract tabular data from PDF files. It allows extracting tables from PDF documents even if they are embedded within text, scanning the pages and detecting table structures automatically.
Some key aspects of using Camelot for tabular data extraction:
- It parses the PDF document and identifies potential table areas based on spacing and separator lines.
- Tables don't need to strictly follow a grid structure to be detected. Camelot can handle skewed tables as well.
- Advanced features like merged cells, spanning rows/columns etc. can also be handled.
- Extracted tables are output as DataFrames in Pandas for further processing.
- Lots of options available for fine-tuning the extraction - like specifying page regions, minimum table sizes, separator regex, etc.
- Can directly export extracted tables to CSV/Excel/JSON/HTML formats.
So in the context of this research, Camelot provides an automated way to separate the tabular data from the PDF documents into structured tables that can then be further enriched and processed. This avoids having to extract all text and then try to make sense of tables later.
DataFrames are the central data structures in the Python Pandas library
used for data analysis and manipulation. Some key aspects:
- A Pandas DataFrame represents tabular data with rows and columns, like a spreadsheet or SQL table.
- Each column can have a different data type, like integers, strings, booleans etc. The data type is automatically inferred.
- Powerful indexing and slicing capabilities to easily subset data frames.
- Built-in methods to perform common data operations like joining, grouping, pivoting, applying functions etc.
- Integrates well with other Python data tools like NumPy, Matplotlib, Scikit-Learn etc.
In the context of tabular data extraction from PDFs:
- Camelot outputs extracted tables as Pandas DataFrames.
- This allows easy processing of the extracted tables compared to raw text extractions.
- The column headings become the column names in the DataFrame.
- Data types are automatically detected - strings, numbers etc.
- DataFrames can then be effortlessly merged, concatenated, analyzed using Python without manual effort.
So in summary, Pandas DataFrames provide a structured container to work with the tabular data programmatically after extracting it from PDFs using Camelot. Their capabilities like slicing, indexing, merging etc. significantly ease the processing and enrichment of extracted tables.
Artifacts and Resources
The paper does not provide full details on the specific artifacts, tools and resources used in the research, but some insights can be gleaned:
Programming Languages:
- Python seems to have been the core language used based on the libraries mentioned.
Libraries/Frameworks:
- Camelot - used for PDF table extraction
- Pandas - for working with extracted tabular data as DataFrames
- Llama-2-chat - the language model integrated into the RAG architecture
- ChatGPT 3.5 API - used for further contextual augmentation
Resources:
- The dataset comprised 200 queries on policy documents to evaluate the techniques.
- PDF documents containing tabular data were used as the corpus for extraction and enrichment.
While full implementation details are not provided, the language and library choices align well with the techniques discussed:
- Python aligns with the data science and ML workflow.
- Camelot and Pandas enable tabular data extraction and processing.
- Llama-2 and ChatGPT 3.5 provide the language model capabilities.
So in summary, while not all artifacts are published, the paper provides clues that standard AI/ML tools like Python, Camelot, Pandas and accessible language models were leveraged to implement their techniques and evaluate on policy documents. The resources seem representative of real-world document-based information retrieval tasks.
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