Search Results for author:
Found 30 papers, 15 papers with code
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation
(2) generate a post including selected products via the MGenNet (Multi-Generator Network).
Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
SSP: Self-Supervised Post-training for Conversational Search
Conversational search has been regarded as the next-generation search paradigm.
UMSE: Unified Multi-scenario Summarization Evaluation
Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods which are specifically designed for each scenario.
A Topic-aware Summarization Framework with Different Modal Side Information
To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.
Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
Contrastive Learning Reduces Hallucination in Conversations
MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality.
Scientific Paper Extractive Summarization Enhanced by Citation Graphs
We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.
Neural Machine Translation with Contrastive Translation Memories
Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios.
Target-aware Abstractive Related Work Generation with Contrastive Learning
The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.
HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling
Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question.
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation
Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.
Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems
This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel.
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement
In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.
Meaningful Answer Generation of E-Commerce Question-Answering
To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.
Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog
Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles
Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.
Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks
For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests.
RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing
There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.
Learning towards Abstractive Timeline Summarization
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
Ranked #1 on
Timeline Summarization
on MTS
Product-Aware Answer Generation in E-Commerce Question-Answering
In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.
Ranked #2 on
Question Answering
on JD Product Question Answer
Abstractive Text Summarization by Incorporating Reader Comments
To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.
Ranked #1 on
Reader-Aware Summarization
on RASG
Iterative Document Representation Learning Towards Summarization with Polishing
In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.
Ranked #14 on
Extractive Text Summarization
on CNN / Daily Mail
