For decades, research in natural language processing
(NLP) has focused on summarization. Sequence-to-sequence
models for abstractive summarization have been studied extensively,
yet generated summaries commonly suffer from fabricated
content, and are often found to be near-extractive. We argue
that, to address these issues, summarizers need to acquire the
co-references that form multiple types of relations over input
sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the
structured knowledge for text usually appears on these relations.
By allowing the decoder to pay different attention to the input
sentences for the same entity at different generation states, the
structured graph representations generate more informative summaries.
In this paper, we propose a hierarchical graph attention
networks (HGATs) for abstractive summarization with a topicsensitive
PageRank augmented graph. Specifically, we utilize dual
decoders, a sequential sentence decoder, and a graph-structured
decoder (which are built hierarchically) to maintain the global
context and local characteristics of entities, complementing each
other. We further design a greedy heuristic to extract salient
users’ comments while avoiding redundancy to drive a model
to better capture entity interactions. Our experimental results
show that our models produce significantly higher ROUGE scores
than variants without graph-based attention on both SSECIF and
CNN/Daily Mail (CNN/DM) datasets.
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Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations. Accordingly, we propose an equivariance learning framework, which encodes tables with a structure-aware self-attention mechanism. This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective. Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells. Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformation-based data augmentation, have seen significant performance drops.
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- Award ID(s):
- 2105329
- NSF-PAR ID:
- 10343362
- Date Published:
- Journal Name:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Page Range / eLocation ID:
- 5037 to 5048
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2022.naacl-main.371
