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Found 20 papers, 10 papers with code
Polarity Calibration for Opinion Summarization
To address this issue and make the summarizer express both sides of opinions, we introduce the concept of polarity calibration, which aims to align the polarity of output summary with that of input text.
Can Large Language Models do Analytical Reasoning?
Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games.
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs.
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization
Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations.
InFoBench: Evaluating Instruction Following Ability in Large Language Models
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention
This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information.
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (MMC-Benchmark), a comprehensive human-annotated benchmark with 9 distinct tasks evaluating reasoning capabilities over charts.
Unsupervised Multi-document Summarization with Holistic Inference
SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4
Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values.
OASum: Large-Scale Open Domain Aspect-based Summarization
Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.
Toward Unifying Text Segmentation and Long Document Summarization
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Ranked #5 on
Text Summarization
on Pubmed
Salience Allocation as Guidance for Abstractive Summarization
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #7 on
Abstractive Text Summarization
on CNN / Daily Mail
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation
While several COPs can be formulated as the prioritization of input items, as is common in the information retrieval, it has not been fully explored how the learning-to-rank techniques can be incorporated into deep RL for COPs.
StreamHover: Livestream Transcript Summarization and Annotation
With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.
Better Highlighting: Creating Sub-Sentence Summary Highlights
Amongst the best means to summarize is highlighting.
Self-Attention Network for Skeleton-based Human Action Recognition
In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3.
Ranked #62 on
Skeleton Based Action Recognition
on NTU RGB+D
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.
A Temporal Sequence Learning for Action Recognition and Prediction
The words are then combined into a sentence to represent the video, as a sentence.
Spatio-Temporal Fusion Networks for Action Recognition
The video based CNN works have focused on effective ways to fuse appearance and motion networks, but they typically lack utilizing temporal information over video frames.
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.
