BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
BIGPATENT dataset offers 1.3 million patent abstracts, advancing high-compression abstractive summarization.
Key Findings
Methodology
The BIGPATENT dataset consists of 1.3 million U.S. patent documents with human-written abstractive summaries. Compared to existing datasets, BIGPATENT summaries exhibit richer discourse structures, evenly distributed salient content, and fewer, shorter extractive fragments.
Key Results
- On BIGPATENT, many models achieve significantly lower ROUGE scores than on news datasets, indicating the need for more advanced models to tackle new challenges.
- Experiments show that existing neural abstractive models produce more abstractive summaries on BIGPATENT but tend to excessively repeat irrelevant discourse entities and often fabricate information.
- 24.1% of entities in BIGPATENT summaries recur, indicating more complex discourse structures.
Significance
The introduction of the BIGPATENT dataset provides new challenges for text summarization research, steering it towards global content modeling, semantic understanding, and discourse-aware text planning. It addresses the issues of simple summary structures and high extractive fragment presence in existing datasets, aiding in generating more informative and coherent summaries.
Technical Contribution
BIGPATENT provides a large-scale patent document dataset supporting the development of more abstractive and coherent text summarization systems. Its features encourage researchers to focus on global content modeling and discourse structure understanding, offering new engineering possibilities.
Novelty
BIGPATENT is the first large-scale patent document summarization dataset with richer discourse structures and evenly distributed salient content, significantly different from news domain datasets.
Limitations
- Existing models perform worse on BIGPATENT compared to news datasets, indicating the need for more complex models to handle its complex discourse structures.
- The length and complexity of patent documents increase processing and training time.
Future Work
Future research directions include developing models that better understand global content and discourse structures, and exploring how BIGPATENT's features can be applied in other domains.
AI Executive Summary
The BIGPATENT dataset consists of 1.3 million U.S. patent documents, providing human-written abstractive summaries. Existing text summarization datasets are mostly from the news domain, with simple summary structures and high extractive fragment presence, limiting the abstraction capability of models. BIGPATENT addresses these issues, with summaries exhibiting richer discourse structures and evenly distributed salient content.
In experiments, many existing models achieve lower ROUGE scores on BIGPATENT than on news datasets, indicating the need for more advanced models to tackle its complex discourse structures. 24.1% of entities in BIGPATENT summaries recur, indicating more complex discourse structures.
BIGPATENT provides new challenges for text summarization research, steering it towards global content modeling, semantic understanding, and discourse-aware text planning. Future research directions include developing models that better understand global content and discourse structures, and exploring how BIGPATENT's features can be applied in other domains.
Deep Analysis
Background
Text summarization is a key task in natural language processing, aiming to extract key information from long texts. Traditional datasets are mostly from the news domain, with simple structures that limit model abstraction capabilities. Recently, researchers have focused on more complex text structures.
Core Problem
Existing datasets often have simple extractive summaries, lacking complex discourse structures, limiting model abstraction capabilities. A dataset with complex discourse structures and evenly distributed salient content is needed.
Innovation
BIGPATENT introduces a large-scale patent document dataset with summaries exhibiting richer discourse structures and evenly distributed salient content, significantly different from news domain datasets.
Methodology
- �� Collect 1.3 million U.S. patent documents
- �� Provide human-written abstractive summaries
- �� Analyze summary discourse structures and salient content distribution
- �� Evaluate existing models on BIGPATENT
Experiments
Experiments use the BIGPATENT dataset to evaluate existing text summarization models. ROUGE scores are used as evaluation metrics, comparing model performance on BIGPATENT and news datasets.
Results
Results show that existing models achieve lower ROUGE scores on BIGPATENT than on news datasets, indicating the need for more complex models to handle its complex discourse structures.
Applications
BIGPATENT can be used to develop more abstractive and coherent text summarization systems, applicable to patent documents, technical literature, and other fields.
Limitations & Outlook
Existing models perform worse on BIGPATENT compared to news datasets, indicating the need for more complex models to handle its complex discourse structures. The length and complexity of patent documents increase processing and training time.
Plain Language Accessible to non-experts
Imagine organizing a massive library where each book has a short introduction. Most existing libraries are filled with news books, with simple introductions that directly quote from the book. BIGPATENT is like a new library filled with patent books, each with a carefully crafted, rich introduction. You need to develop new methods to organize these books because they are more complex than news books.
ELI14 Explained like you're 14
Imagine playing a game where you need to extract important information from a pile of complex patent documents. Existing game levels are news articles, which are simpler and just require direct quoting. BIGPATENT is a new level with more complex patent documents and evenly distributed information. You need to level up your skills to score high in this new level!
Glossary
ROUGE Score
A metric for evaluating the quality of automatic summaries based on overlap with reference summaries.
Used to evaluate model performance on BIGPATENT.
Abstractive Summary
A summary that is rephrased and compressed differently from the input text.
Type of summary provided by BIGPATENT.
Discourse Structure
The organization of information in a text, including entity repetition and connections.
More complex discourse structures in BIGPATENT summaries.
Salient Content
The most important information in a text, usually the core of a summary.
Evenly distributed salient content in BIGPATENT.
Extractive Fragment
Text segments directly quoted from the input text.
Fewer and shorter extractive fragments in BIGPATENT summaries.
Open Questions Unanswered questions from this research
- 1 How to apply BIGPATENT's features in other domains to improve summary abstraction and coherence.
- 2 Limitations of existing models in handling complex discourse structures and how to improve.
Applications
Immediate Applications
Patent Document Summarization
BIGPATENT can be used to generate abstractive summaries of patent documents, helping researchers quickly access key information.
Long-term Vision
Cross-Domain Text Summarization
Utilize BIGPATENT's features to develop text summarization systems applicable to various domains, enhancing information retrieval efficiency.
Abstract
Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article's global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.