42 citations · 75 across the 17 of their papers we have counts for
16 papers · 1 filter
SiReRAG: Indexing Similar and Related Information for Multihop Reasoning
Nan Zhang, Prafulla Kumar Choubey, Alexander Fabbri +5
Indexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarit…
Evaluating Cultural and Social Awareness of LLM Web Agents
Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal +4
As large language models (LLMs) expand into performing as agents for real-world applications beyond traditional NLP tasks, evaluating their robustness becomes increasingly importan…
Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems
Philippe Laban, Alexander R. Fabbri, Caiming Xiong +1
LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging,…
Lexical Repetitions Lead to Rote Learning: Unveiling the Impact of Lexical Overlap in Train and Test Reference Summaries
Prafulla Kumar Choubey, Alexander R. Fabbri, Caiming Xiong +1
Ideal summarization models should generalize to novel summary-worthy content without remembering reference training summaries by rote. However, a single average performance score o…
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization
Yixin Liu, Alexander R. Fabbri, Jiawen Chen +7
While large language models (LLMs) can already achieve strong performance on standard generic summarization benchmarks, their performance on more complex summarization task setting…
Fair Abstractive Summarization of Diverse Perspectives
Yusen Zhang, Nan Zhang, Yixin Liu +9
People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and pol…
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles
Kung-Hsiang Huang, Philippe Laban, Alexander R. Fabbri +4
Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse inform…
CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing
Divyansh Agarwal, Alexander R. Fabbri, Simeng Han +7
This paper introduces the shared task of summarizing documents in several creative domains, namely literary texts, movie scripts, and television scripts. Summarizing these creative…
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Prafulla Kumar Choubey, Alexander R. Fabbri, Jesse Vig +3
Hallucination is a known issue for neural abstractive summarization models. Recent work suggests that the degree of hallucination may depend on errors in the training data. In this…
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Alexander R. Fabbri, Simeng Han, Haoyuan Li +5
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typicall…
SummEval: Re-evaluating Summarization Evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann +3
The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.…
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
Alexander R. Fabbri, Patrick Ng, Zhiguo Wang +2
Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has bee…
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
Michihiro Yasunaga, Jungo Kasai, Rui Zhang +4
Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community. Thi…
Sarcasm Analysis using Conversation Context
Debanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan
Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker's sarcastic intent is not always apparent without addit…
The Role of Conversation Context for Sarcasm Detection in Online Interactions
Debanjan Ghosh, Alexander Richard Fabbri, Smaranda Muresan
Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker's sarcastic intent is not always obvious without additional…