Papers by Shahbaz Syed
Target Inference in Argument Conclusion Generation (2020.acl-main)
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| Challenge: | Existing approaches focus on generating single claims, but there are limitations. |
| Approach: | They propose to use a triplet neural network to infer a conclusion's target from premises' targets and a neural network for a new target. |
| Outcome: | The proposed approach outperforms baselines on two domains. |
Counter-Argument Generation by Attacking Weak Premises (2021.findings-acl)
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| Challenge: | a recent work explores the generation of counter-arguments by undermining one of its premises . identifying the argument's weak premises is key to effective countering, we hypothesize . |
| Approach: | They propose a pipeline approach that first assesses the argument's weak premises and generates a counter-argument undermining the weakest among them. |
| Outcome: | The proposed approach undermins arguments by attacking weak premises . human annotators favor the proposed approach over state-of-the-art approaches . |
Generating Informative Conclusions for Argumentative Texts (2021.findings-acl)
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| Challenge: | Argumentative texts often omit explicit conclusions, expecting readers to infer them rather . a corpus of 136,996 arguments is compiled and used to generate informative conclusions . |
| Approach: | They propose to generate informative conclusions from a large-scale corpus of argumentative texts . they propose to use argumentative knowledge to augment the corpus and refine the model . |
| Outcome: | The proposed corpus of argumentative texts and their conclusions is compiled and analyzed . the results show that the proposed model is informative and concise . |
Summary Explorer: Visualizing the State of the Art in Text Summarization (2021.emnlp-demo)
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| Challenge: | Automatic text summarization is the task of generating a summary of a long text by condensing it to its most important parts. |
| Approach: | They propose a tool to visually explore document summarization systems based on three well-known summary quality criteria . |
| Outcome: | The proposed tool compiles outputs of 55 state-of-the-art document summarization approaches and visually explores them during a qualitative assessment. |
On Synthesizing Data for Context Attribution in Question Answering (2025.acl-long)
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Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed, Christopher Malon, Sebastien Nicolas, Chia-Chien Hung, Timo Sztyler, Verena Heußer, Wiem Ben Rim, Masafumi Enomoto, Kunihiro Takeoka, Masafumi Oyamada, Goran Glavaš, Carolin Lawrence
| Challenge: | Large Language Models (LLMs) have a tendency to hallucinate, resulting in false or misleading answers. |
| Approach: | They propose a novel generative strategy for synthesizing context attribution data. |
| Outcome: | The proposed approach is highly effective for fine-tuning small LMs for context attribution in different QA tasks and domains. |
Indicative Summarization of Long Discussions (2023.emnlp-main)
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| Challenge: | Using large language models, we generate indicative summaries instead of informative summary for long discussions. |
| Approach: | They propose an unsupervised approach to generating indicative summaries using large language models using large-scale language models. |
| Outcome: | The proposed method clusters argument sentences, generates abstractive summaries, and classifies the generated cluster labels into argumentation frames. |
Citance-Contextualized Summarization of Scientific Papers (2023.findings-emnlp)
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| Challenge: | Current automatic summarization approaches generate abstracts, but abstracts do not show relationship between paper and references. |
| Approach: | They propose a contextualized summarization approach that generates an informative summary . they extract and model the citances of a paper, retrieve relevant passages from cited papers, and generate abstractive summaries tailored to each citance. |
| Outcome: | The proposed method extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. |
Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness (2020.acl-main)
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| Challenge: | Several studies have examined persuasiveness in debates by probing the main factors for establishing persuasion, particularly regarding the role of linguistic features of debaters' arguments. |
| Approach: | They propose to model debaters’ prior beliefs, interests, and personality traits based on their previous activity without dependence on explicit user profiles or questionnaires. |
| Outcome: | The proposed model improves persuasiveness prediction and debater resistance to persuasion. |
SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models (2022.emnlp-demos)
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| Challenge: | Summary Workbench is a tool for developing and evaluating text summarization models. |
| Approach: | They propose a tool for developing and evaluating text summarization models that integrates with Docker plugins and provides visual analysis of models’ strengths and weaknesses. |
| Outcome: | The proposed model and evaluation measures can be easily integrated as Docker-based plugins and provide insights into the models’ strengths and weaknesses. |
News Editorials: Towards Summarizing Long Argumentative Texts (2020.coling-main)
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| Challenge: | Using news summarization, we aim to target opinionated articles with a well-defined argumentation structure. |
| Approach: | They present a corpus of carefully curated summaries for 266 news editorials. |
| Outcome: | The summarization of opinionated articles with a well-defined argumentation structure is evaluated using a tailored annotation scheme. |
Modeling Appropriate Language in Argumentation (2023.acl-long)
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| Challenge: | Existing research on offensive language has not been systematically addressed in debates . a new taxonomy of 14 dimensions determines inappropriate language in online discussions . |
| Approach: | They propose a taxonomy of 14 dimensions that determine inappropriate language in online discussions . they build on arguments quality corpora and annotate them on a corpus of 2191 arguments . |
| Outcome: | The proposed taxonomy covers the concept of appropriateness comprehensively, showing plausible correlations with argument quality dimensions. |
TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization (2024.eacl-demo)
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| Challenge: | TL;DR Progress is a literature explorer designed specifically for the text summarization literature. |
| Approach: | They propose to organize 514 papers based on a comprehensive annotation scheme for text summarization approaches and a fine-grained, faceted search. |
| Outcome: | The proposed tool organizes 514papers based on a comprehensive annotation scheme for text summarization approaches and enables fine-grained, faceted search. |
Retrieval of the Best Counterargument without Prior Topic Knowledge (P18-1)
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| Challenge: | ad-hominem attacks are the most common form of argumentation in real life . |
| Approach: | They hypothesize that the best counterargument invokes the same aspects as the argument while having the opposite stance. |
| Outcome: | The proposed model is independent from the topic at hand, i.e., it applies to arbitrary arguments. |