AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
AQuaMuSe method automatically generates 5,519 query-based multi-document summarization datasets.
Key Findings
Methodology
AQuaMuSe automatically mines query-based multi-document summarization (qMDS) examples from question answering datasets and large document corpora. It generates dual datasets for extractive and abstractive summaries, validated using Google Natural Questions and Common Crawl.
Key Results
- Result 1: Generated 5,519 query-based summaries, each associated with an average of 6 input documents selected from a 355M Common Crawl index.
- Result 2: Dataset demonstrated good performance and diversity in baseline summarization model experiments.
- Result 3: Human evaluation validated dataset quality, with 85.18% examples marked as relevant.
Significance
The study fills the gap of training and evaluation datasets for qMDS, advancing NLP and information retrieval fields. It addresses the inadequacies of existing datasets in form and scale, providing a new experimental platform for researchers.
Technical Contribution
Technical contributions include introducing a scalable automated method for generating qMDS datasets, supporting both extractive and abstractive summaries, and achieving efficient semantic matching and document selection in large corpora.
Novelty
AQuaMuSe is the first method capable of generating both extractive and abstractive qMDS datasets, with significant innovations in automation and scalability compared to existing methods.
Limitations
- Limitation 1: The generated dataset may lack sufficient semantic diversity in certain scenarios.
- Limitation 2: Challenges may arise in generating long summaries in specific domains.
Future Work
Future work includes extending the method to accommodate more types of question answering datasets and corpora, and improving semantic matching algorithms to enhance summary quality and diversity.
AI Executive Summary
The AQuaMuSe method addresses the inadequacies of existing datasets in form and scale by automatically mining query-based multi-document summarization (qMDS) examples from question answering datasets and large document corpora. It generates dual datasets for extractive and abstractive summaries, validated using Google Natural Questions and Common Crawl.
The study generated 5,519 query-based summaries, each associated with an average of 6 input documents selected from a 355M Common Crawl index. The dataset's quality was validated through baseline summarization model experiments and human evaluation, with 85.18% of examples marked as relevant.
AQuaMuSe's innovation lies in its automation and scalability in dataset generation, providing a new experimental platform for NLP and information retrieval research. Future work will include extending the method to accommodate more types of question answering datasets and corpora, and improving semantic matching algorithms to enhance summary quality and diversity.
Deep Analysis
Background
Multi-document summarization is crucial in NLP, especially for information retrieval and user query applications. Existing datasets are mostly single-document summaries, lacking applicability for multi-document and query-based scenarios.
Core Problem
The core problem is the lack of large-scale, high-quality qMDS datasets, limiting research development. Generating long summaries is particularly challenging as it requires extracting and integrating information from multiple documents.
Innovation
AQuaMuSe's core innovation is its automated and scalable dataset generation capability, able to produce both extractive and abstractive summaries, with efficient semantic matching and document selection in large corpora.
Methodology
- �� Use Google Natural Questions dataset as the query source.
- �� Utilize Common Crawl corpus for document selection.
- �� Perform semantic matching using Universal Sentence Encoder.
- �� Generate extractive and abstractive summary datasets.
Experiments
Experiments used Google Natural Questions and Common Crawl corpora, generating 5,519 query-based summaries. Dataset quality was validated through baseline model experiments and human evaluation.
Results
The dataset showed good performance in baseline model experiments, with 85.18% of examples marked as relevant, validating its quality and diversity.
Applications
This dataset can be used to train and evaluate multi-document summarization models, particularly in information retrieval and user query applications.
Limitations & Outlook
The method may lack sufficient semantic diversity in certain scenarios, and challenges may arise in generating long summaries. Future work will include improving semantic matching algorithms.
Plain Language Accessible to non-experts
Imagine you're in a library wanting to quickly learn about a topic. You have many books and articles but no time to read each one. AQuaMuSe acts like a super librarian, picking the most relevant parts from these books and articles to summarize into a concise report for you. This way, you get the information you need quickly without wasting time on unrelated content.
ELI14 Explained like you're 14
Imagine you're playing a game and need to quickly find information about a topic. AQuaMuSe is like your game assistant, finding the most relevant content from lots of sources and summarizing it for you. This way, you can quickly understand what you need without getting bogged down in unimportant details. Isn't that cool?
Glossary
AQuaMuSe
A method for automatically generating query-based multi-document summarization datasets.
Used to mine qMDS examples from question answering datasets and large document corpora.
qMDS
Query-based multi-document summarization.
Requires extracting information from multiple documents to generate relevant summaries.
Google Natural Questions
An open-domain question answering dataset containing real user queries.
Used as the query source for generating qMDS datasets.
Common Crawl
An open corpus containing a large number of web pages.
Used to select documents relevant to queries.
Universal Sentence Encoder
A tool for semantic encoding of sentences.
Used for semantic matching in AQuaMuSe.
Open Questions Unanswered questions from this research
- 1 How to enhance semantic diversity in generated summaries? Current methods may not cover all relevant information in some cases.
Applications
Immediate Applications
Information Retrieval
Helps search engines generate more relevant summaries, improving user experience.
Long-term Vision
Intelligent Assistants
Provides more accurate information summaries for intelligent assistants, enhancing their ability to answer complex questions.
Abstract
Summarization is the task of compressing source document(s) into coherent and succinct passages. This is a valuable tool to present users with concise and accurate sketch of the top ranked documents related to their queries. Query-based multi-document summarization (qMDS) addresses this pervasive need, but the research is severely limited due to lack of training and evaluation datasets as existing single-document and multi-document summarization datasets are inadequate in form and scale. We propose a scalable approach called AQuaMuSe to automatically mine qMDS examples from question answering datasets and large document corpora. Our approach is unique in the sense that it can general a dual dataset -- for extractive and abstractive summaries both. We publicly release a specific instance of an AQuaMuSe dataset with 5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl. Extensive evaluation of the dataset along with baseline summarization model experiments are provided.