End-to-End Encoder-Decoder for Interleaved Text Summarization
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Solution Overview
Problem
Existing systems for automatically summarizing interleaved conversations in electronic communication systems often introduce errors during the disentanglement process, which degrade the overall performance of the summarization task, making it difficult for users to follow and identify the essence of different threads.
Innovation Solution
An end-to-end trainable encoder-decoder system that uses hierarchical encoding and decoding methods, eliminating the need for disentanglement, and incorporating a hierarchical attention mechanism to generate multi-sentence summaries directly from interleaved texts, enhancing performance by 8% on synthetic datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a two-step process with disentanglement is used to summarize interleaved conversations, then the summarization task can be performed, but errors are introduced during disentanglement that degrade overall performance
Solution Approach 1:
The patent merges the disentanglement step and summarization step into a single unified end-to-end trainable model. Instead of separately disentangling threads and then summarizing each thread, the model directly generates summaries of interleaved conversations by jointly learning both tasks, thereby eliminating error propagation between steps while maintaining process feasibility.
2Loss of information
If disentanglement is performed on interleaved posts, then separate threads can be identified, but errors from disentanglement are propagated to the summarization task
Solution Approach 1:
The patent extracts and removes the problematic disentanglement step from the processing pipeline. By taking out the intermediate disentanglement operation that causes error propagation, the model directly processes interleaved posts to generate summaries without introducing separation errors, while still achieving effective thread identification through the attention mechanism.
3Productivity
If sentence compression method is used to compress threads into single sentence summaries, then summarization can be achieved, but the complexity of handling interleaved texts remains high
Solution Approach 1:
The patent changes the output parameter from single-sentence summaries to multi-sentence summaries. This parameter change allows the model to capture more nuanced information and maintain higher accuracy while handling interleaved texts, as multi-sentence summaries can represent complex thread structures better without requiring overly complex processing mechanisms.
Data Source
AI summary
A method and system for generating summaries of posts of interleaved text are provided. The method includes embedding, by a first neural network, each post through word-to-word encoding; embedding, by a second neural network, overall content of the plurality of posts through post-to-post encoding based on the word-to-word encoding of each post; generating, by at least a third neural network, a summary of the at least one thread through word-to-word decoding based on the overall content embedding of the plurality of posts; and displaying the summary of the at least one thread to a user.