| Challenge: | Using neural text generation, we generate To-Do items from emails where the sender has promised to perform an action. |
| Approach: | They propose a task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. |
| Outcome: | The proposed task obtains BLEU and ROUGE scores of 0.23 and 0.63 for the task. |
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| Challenge: | Existing to-do item generation models focus on generating action mentions to provide more structured summaries of email text. |
| Approach: | They propose a learning to highlight and summarize framework to learn to identify the most salient text and actions and incorporate these structured representations to generate more faithful to-do items. |
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This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (P19-1)
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| Challenge: | Existing research tracks on email use focus on email summarization, email keyword extraction and action detection. |
| Approach: | They propose to use email body to automatically generate an email subject line from the email body. |
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Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)
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| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
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Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
Data-to-text Generation with Macro Planning (2021.tacl-1)
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| Challenge: | Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently . |
| Approach: | They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets . |
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Seg2Act: Global Context-aware Action Generation for Document Logical Structuring (2024.emnlp-main)
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Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, Le Sun
| Challenge: | Document logical structuring is crucial for document intelligence due to the complexity of text segment dependencies in the document. |
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Learning to Select, Track, and Generate for Data-to-Text (P19-1)
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, Hiroya Takamura
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Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
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| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Approach: | They propose a pipeline that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
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ToTTo: A Controlled Table-To-Text Generation Dataset (2020.emnlp-main)
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Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
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Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation (2021.acl-long)
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| Challenge: | Experimental results show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering. |
| Approach: | They propose to build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph. |
| Outcome: | The proposed method outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering. |