Papers with WikiHow
A Computational Analysis of Vagueness in Revisions of Instructional Texts (2021.eacl-srw)
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| Challenge: | We analyze edits that involve cases of vagueness in instructional texts . we extract and analyze version pairs of an instruction before and after a revision . |
| Approach: | They propose to extract and analyze edits that involve cases of vagueness in instructions . they adopt a pairwise ranking task to show improvements over existing baselines . |
| Outcome: | The proposed model can distinguish between two versions of an instruction in a noisy dataset. |
GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization (2023.emnlp-main)
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| Challenge: | Existing models that mimic human summarization techniques are difficult to imitate. |
| Approach: | They propose an adaptive model that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques. |
| Outcome: | The proposed model outperforms baselines on WikiHow and on other datasets. |
WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering (2023.acl-long)
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| Challenge: | Answering non-factoid questions (NFQs) is a challenging task, requiring passage-level answers that are difficult to construct and evaluate. |
| Approach: | They propose a multi-document NFQA benchmark built on WikiHow, a website dedicated to answering “how-to” questions. |
| Outcome: | The proposed framework includes 11,746 human-written answers along with 74,527 supporting documents. |
Non-Sequential Graph Script Induction via Multimedia Grounding (2023.acl-long)
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| Challenge: | Existing scripts for everyday tasks are presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life. |
| Approach: | They propose to use loosely aligned videos to train a non-sequential graph script induction task by using a multimodal framework to ground procedural videos to WikiHow textual steps. |
| Outcome: | The proposed model outperforms the WikiHow linear baseline by 48.76% . it can predict future steps given a partial step sequence and generate explicit graph scripts . |
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization (2020.findings-emnlp)
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| Challenge: | a lack of high quality multilingual data for cross-lingual summarization is a costly endeavor since it requires humans to read, comprehend, condense, and paraphrase entire articles. |
| Approach: | They propose to use a large-scale, multilingual dataset to evaluate cross-lingual abstractive summarization systems. |
| Outcome: | The proposed method significantly outperforms baseline approaches while being more cost efficient during inference. |
Multi-hop Inference for Question-driven Summarization (2020.emnlp-main)
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| Challenge: | Existing methods for summarizing source document for non-factoid questions are lacking in factoidic QA. |
| Approach: | They propose a question-driven abstractive summarization method that incorporates multi-hop reasoning into question-based summarizing. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two non-factoid QA datasets. |
Improving Faithfulness by Augmenting Negative Summaries from Fake Documents (2022.emnlp-main)
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| Challenge: | Current abstractive summarization systems tend to hallucinate unfaithful content . however, the most common method does not disentangle factual errors from other errors. |
| Approach: | They propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model. |
| Outcome: | The proposed method improves faithfulness without sacrificing informativeness . it incorporates negative samples into training, and produces faithful/unfaithful summaries . |
SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)
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| Challenge: | Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge. |
| Approach: | They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information. |
| Outcome: | The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights. |