Papers Book summarization
“Book summarization” 태그가 달린 논문 13편 · 필터 해제
Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?
Language models handle increasingly long contexts for tasks such as book summarization, but this leads to growing memory costs for the key-value (KV) cache. Many prior works have proposed ways of discarding KVs from memo…
Book summarizationLong-Context UnderstandingKV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches
Long context capability is a crucial competency for large language models (LLMs) as it mitigates the human struggle to digest long-form texts. This capability enables complex task-solving scenarios such as book summariza…
Book summarizationQuantizationIs It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. However, many disparate use-cases are grouped …
AllBook summarizationDescriptiveTraining-Free Exponential Context Extension via Cascading KV Cache
The transformer's context window is vital for tasks such as few-shot learning and conditional generation as it preserves previous tokens for active memory. However, as the context lengths increase, the computational cost…
Book summarizationComputational EfficiencyFew-Shot LearningMMLU+2Attention Score is not All You Need for Token Importance Indicator in KV Cache Reduction: Value Also Matters
Scaling the context size of large language models (LLMs) enables them to perform various new tasks, e.g., book summarization. However, the memory cost of the Key and Value (KV) cache in attention significantly limits the…
AllBook summarizationLeave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention …
Book summarizationLanguage ModelingLanguage ModellingRetrievalLOCOST: State-Space Models for Long Document Abstractive Summarization
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies. We propose LOCOST: an encoder-decoder architecture based on state-space models for con…
Abstractive Text SummarizationBook summarizationConditional Text GenerationDecoder+2Echoes from Alexandria: A Large Resource for Multilingual Book Summarization
In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features. The task of full-book summarization presents additional challenges w…
Book summarizationText SummarizationHybrid Long Document Summarization using C2F-FAR and ChatGPT: A Practical Study
Text summarization is a downstream natural language processing (NLP) task that challenges the understanding and generation capabilities of language models. Considerable progress has been made in automatically summarizing…
ArticlesBenchmarkingBook summarizationDocument Summarization+2Unlimiformer: Long-Range Transformers with Unlimited Length Input
Since the proposal of transformers, these models have been limited to bounded input lengths, because of their need to attend to every token in the input. In this work, we propose Unlimiformer: a general approach that wra…
Book summarizationCPUDecoderGPUEnhancing Large Language Model with Self-Controlled Memory Framework
Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information. To address this limitation, in this paper, we propose the Self-Controll…
Book summarizationDocument SummarizationInstruction FollowingLanguage Modeling+4Transformer Based Implementation for Automatic Book Summarization
Document Summarization is the procedure of generating a meaningful and concise summary of a given document with the inclusion of relevant and topic-important points. There are two approaches: one is picking up the most r…
Abstract generationAbstractive Text SummarizationBook summarizationDocument Summarization+1New Alignment Methods for Discriminative Book Summarization
We consider the unsupervised alignment of the full text of a book with a human-written summary. This presents challenges not seen in other text alignment problems, including a disparity in length and, consequent to this,…
Book summarization