HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models excel in various natural language tasks in English, but their performance in low-resource languages like Hebrew remains unclear. |
| Approach: | They propose a benchmark dataset specifically designed for Hebrew abstractive text summarization that combines 10,000 article-summary pairs from Hebrew news websites. |
| Outcome: | The proposed dataset shows that it presents distinct difficulties even for state-of-the-art LLMs. |
Similar Papers
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing work on abstractive dialogue summarizations has focused on news summarizing but there is no such comprehensive dataset. |
| Approach: | They propose to use a chat-dialogues corpus with abstractive dialogue summaries to generate a short version of text that covers the main points succinctly. |
| Outcome: | The proposed dataset achieves higher ROUGE scores than the model-generated summaries of news, compared with human evaluators' judgement. |
GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets are limited to newswire text, which is a fraction of extant genres in general and on the Web. |
| Approach: | They present a small but carefully crafted dataset of English summaries in 12 written and spoken genres for evaluation of abstractive summarization. |
| Outcome: | The proposed dataset of English summaries in 12 written and spoken genres is compared with human outputs and compared to untuned and prompt-based approaches. |
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs (2023.acl-long)
Copied to clipboard
| Challenge: | a large-scale cross-lingual summarization dataset is available for free . a cross-linguistic summarizing model can be trained in any target language . |
| Approach: | They propose a multistage data sampling algorithm to train a cross-lingual summarization model capable of summarizing an article in any target language. |
| Outcome: | The proposed model outperforms baseline models on ROUGE and LaSE. |
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu (2022.lrec-1)
Copied to clipboard
| Challenge: | a number of recent datasets for summarisation, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. |
| Approach: | They propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. |
| Outcome: | The proposed pipeline can be applied to scraped datasets to extract better quality articles-summaries pairs. |
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021.findings-acl)
Copied to clipboard
Tahmid Hasan, Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | XL-Sum dataset covers 44 languages ranging from low to high-resource . Xl-SUM is highly abstractive, concise, and of high quality . |
| Approach: | They present a dataset comprising 1 million professionally annotated article-summary pairs from BBC . they fine-tune a pretrained multilingual model with XL-Sum and experiment on multilingual and lowresource tasks. |
| Outcome: | The proposed dataset is highly abstractive, concise, and of high quality . it shows higher scores on 10 languages than similar datasets compared to monolingual ones . |
Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text (2026.acl-long)
Copied to clipboard
| Challenge: | CR methods originally designed for English struggle with Morphologically Rich Languages (MRLs) a single token in Hebrew may consist of multiple anaphors, and word/morpheme boundary discrepancies make mention detection and coreference resolution difficult in MRLs. |
| Approach: | They propose a CR dataset that identifies mentions at word, sub-word and multi-word levels and an evaluation protocol that directly addresses word/morpheme boundary discrepancies. |
| Outcome: | The proposed evaluation protocol directly addresses word/morpheme boundary discrepancies in Modern Hebrew, an MRL rich with complex words and pronominal clitics. |
Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages (2026.findings-eacl)
Copied to clipboard
| Challenge: | Recent studies suggest that summarization in English may be solved, or even "dead" However, there are no accessible, high-quality summarizing datasets in under-represented languages. |
| Approach: | They propose a method for collecting naturally occurring summaries via front-page teasers, where editors summarize full length articles. |
| Outcome: | The proposed method is suited to varying linguistic resources and is available in seven languages. |
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have advanced tasks like text summarization, but their size and computational demands limit their use in resource-constrained and privacy-centric settings. |
| Approach: | They propose a framework for distilling LLMs’ text summarization abilities into a compact, local model using a curriculum learning strategy that evolves from simple to complex tasks. |
| Outcome: | The proposed framework outperforms baseline models on CNN/DailyMail, XSum, and ClinicalTrial, and improves interpretability by providing insights into the summarization rationale. |
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on summarizing news documents or structured documents. |
| Approach: | They propose to use a large-scale narrative summarization dataset to encourage research . they find there is a performance gap between humans and the models on NarraSum . |
| Outcome: | The proposed dataset shows that humans and state-of-the-art models perform poorly when summarizing a narrative . it contains 122K narratives collected from synopses of movies and TV episodes with diverse genres . |
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)
Copied to clipboard
| Challenge: | Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles. |
| Approach: | They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. |
| Outcome: | The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input. |