Key Phrase Generation via Document Reconstruction Learning
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Solution Overview
Problem
Existing key phrase generation models require annotated key phrases for accurate learning, but these are often unavailable, leading to low accuracy in key phrase generation, especially when using automatically generated pseudo key phrases.
Innovation Solution
A learning system that includes an acquisition unit for obtaining document data, a generation unit for generating key phrases, a restoration unit for restoring document data from generated key phrases, and a learning unit that learns parameters based on the original and restored document data, allowing for accurate key phrase generation without manually annotated key phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually annotated key phrases are used for training, then key phrase generation accuracy is improved, but the complexity of data preparation and time consumption increase significantly
Solution Approach 1:
The system uses the language model itself to generate pseudo key phrases from documents, eliminating the need for external manual annotation. The model generates key phrases autonomously during the training process, allowing it to serve its own data preparation needs and thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system performs preliminary generation of pseudo key phrases before the actual training process. By pre-generating training data using the language model, the system prepares the necessary training corpus in advance, avoiding time-consuming manual annotation while ensuring sufficient training data is available for accurate model learning.
2Productivity
If automatically generated pseudo key phrases are used for training, then data preparation time is reduced, but key phrase generation accuracy decreases
Solution Approach 1:
The system implements a feedback mechanism where the language model generates key phrases, these generated key phrases are then used to reconstruct documents, and the reconstruction quality feeds back into the training process. This feedback loop allows the model to iteratively improve its key phrase generation accuracy while maintaining high data preparation efficiency.
Solution Approach 2:
The system introduces a new dimension to the training process by using document reconstruction as an intermediate step. Instead of directly training on pseudo key phrases, the system trains on the relationship between documents and their reconstructed versions, adding a dimensional transformation that preserves accuracy while maintaining efficiency.
3Measurement precision
If manually annotated key phrases are required, then training data quality is improved, but the adaptability to domains without annotations deteriorates
Solution Approach 1:
The system creates a universal training approach that works across different domains without requiring domain-specific manual annotations. The language model can generate pseudo key phrases for any domain, making the training process universally applicable and thereby improving domain adaptability while maintaining sufficient data quality through the reconstruction feedback mechanism.
Data Source
AI summary
A learning system includes: an acquisition unit that obtains document data; a generation unit that generates a key phrase from the document data; a restoration unit that restores the document data from the generated key phrase; and a learning unit that learns parameters of the generation unit on the basis of the document data and the restored document data. According to such a learning system, high-precision learning can be performed even when there is no key phrase as a correct answer.