Papers by Rob Procter
Template-based Abstractive Microblog Opinion Summarization (2022.tacl-1)
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| Challenge: | Existing work on Twitter uses extractive summarization to filter through information, but this approach often includes incomplete or redundant information. |
| Approach: | They propose to use Twitter data to generate 3100 gold-standard opinion summaries. |
| Outcome: | The proposed method outperforms previous work on extractive summarization models and fine-tunes to improve performance. |
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling (2024.lrec-main)
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| Challenge: | Existing topic models rely on bag-of-words (BOW) representations to capture word order information. |
| Approach: | They propose a neural topic model that integrates contextualized word embeddings from BERT to learn the topic vector of a document without BOW information. |
| Outcome: | The proposed model generates more coherent and meaningful topics compared to existing models while accommodating unseen words in newly encountered documents. |
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System (2023.eacl-main)
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| Challenge: | Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes and comparing different models. |
| Approach: | They propose a human-in-the-loop topic modeling system that integrates users’ knowledge into the modelling process, enabling them to refine the model iteratively. |
| Outcome: | The proposed system is based on a series of user studies to assess its performance in progressively more realistic applications. |
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering (2022.emnlp-demos)
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| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
| Approach: | They propose a pipeline for validating and training QA data and an interface for human annotation. |
| Outcome: | The proposed pipeline improves QA performance on domain-specific datasets while preserving the accuracy of the model. |
Unsupervised Opinion Summarisation in the Wasserstein Space (2022.emnlp-main)
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| Challenge: | Recent work on opinion summarisation of social media posts has focused on reviews . however, it is important to capture user opinions in online discussions over specific topics . |
| Approach: | They propose an unsupervised opinion summarisation model which uses the Wasserstein distance to generate a single summary from a group of documents. |
| Outcome: | The proposed model outperforms the state-of-the-art on ROUGE metrics and produces the best summaries with respect to meaning preservation according to human evaluations. |
A Query-Driven Topic Model (2021.findings-acl)
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| Challenge: | Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. |
| Approach: | They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics. |
| Outcome: | The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics. |
PANACEA: An Automated Misinformation Detection System on COVID-19 (2023.eacl-demo)
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Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He
| Challenge: | Using social media and fact-checking to detect misinformation is not enough to prevent the spread of false information. |
| Approach: | They propose a web-based misinformation detection system PANACEA which has two modules, fact-checking and rumour detection. |
| Outcome: | The system outperforms state-of-the-art methods and adapts graph convolutional networks model to detect rumours based on tweets rather than knowledge bases. |
Evaluation of Thematic Coherence in Microblogs (2021.acl-long)
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| Challenge: | Recent work on grouping together views about tweets expressing opinions about the same entities has been criticized for their lack of thematic coherence. |
| Approach: | They propose to use a corpus of microblogs representing opinions about the same topics within the same time window to evaluate thematic coherence. |
| Outcome: | The proposed method outperforms surface level metrics, topic model coherence and text generation metrics (TGMs) but is not as reliable as TGMs due to being less sensitive to time windows. |