Papers by Mengwen Liu
Multi-Task Networks with Universe, Group, and Task Feature Learning (P19-1)
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| Challenge: | In multi-task learning, multiple related tasks are learned together. |
| Approach: | They propose methods that take advantage of natural groupings of related tasks . they propose parallel and serial architectures that can learn different feature spaces . |
| Outcome: | The proposed methods improve performance on natural language understanding (NLU) tasks. |
REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset (2024.acl-long)
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| Challenge: | generating accurate and faithful multimodal summaries is challenging due to lack of appropriate multimodal datasets . large language models excel at synthesizing key information from diverse sources, but lack of adequate multimodal data sets for fine-tuning . |
| Approach: | They propose a dataset specifically designed for image-text multimodal summarization . they generate summaries from Wikipedia sections and corresponding images and evaluate them . |
| Outcome: | The proposed dataset improves summary quality by training a critic model on human annotations and using its predictions to remove low-quality summaries. |
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters (2021.naacl-main)
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| Challenge: | Abstractive multi-document summarization (MDS) is a task that has seen advances with the introduction of large-scale datasets and powerful Transformer-based models. |
| Approach: | They propose an efficient graph-enhanced approach to multi-document summarization with an encoder-decoder Transformer model. |
| Outcome: | The proposed model scales to large input documents and improves on a multi-document dataset. |
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations (2022.naacl-main)
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| Challenge: | Recent studies show that abstractive summarization approaches generate summaries that are not factually consistent with the source document. |
| Approach: | They propose a method that decomposes the document and summary into structured meaning representations (MRs) MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form . |
| Outcome: | The proposed method outperforms existing methods on benchmarks for factuality evaluation. |
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization (2023.findings-eacl)
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| Challenge: | Abstractive summarization models generate fluent and well-formed output but lack semantic faithfulness, or factuality, with respect to the input documents. |
| Approach: | They propose new factuality metrics that adjust for the degree of abstractiveness . they propose to visualize the rates of change in factual as we gradually increase abstractiveity . |
| Outcome: | The proposed models generate fluent and well-formed summaries but lack semantic faithfulness, or factuality, with respect to the input documents. |
Faithfulness-Aware Decoding Strategies for Abstractive Summarization (2023.eacl-main)
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| Challenge: | Existing studies on faithfulness of abstractive summarization have focused on decoding strategies. |
| Approach: | They propose two faithfulness-aware generation methods to further improve faithfulness . they propose to use a distillation approach to generate faithful summaries with greedy decoding . |
| Outcome: | The proposed methods improve faithfulness across two datasets as evaluated by automatic faithfulness metrics and human evaluation. |