A Relevant Content Filtering Based Framework For Data Stream Summarization
Social media platforms are a rich source of information these days, however, of all the available information, only a small fraction is of users' interest. To help users catch up with the latest topics of their interests from the large amount of information available in social media, we present a relevant content filtering based framework for data stream summarization. More specifically, given the topic or event of interest, this framework can dynamically discover and filter out relevant information from irrelevant information in the stream of text provided by social media platforms. It then further captures the most representative and up-to-date information to generate a sequential summary or event story line along with the evolution of the topic or event. Our framework does not depend on any labeled data, it instead uses the weak supervision provided by the user, which matches the real scenarios of users searching for information about an ongoing event. We experimented on two real events traced by a Twitter dataset from TREC 2011. The results verified the effectiveness of relevant content filtering and sequential summary generation of the proposed framework. It also shows its robustness of using the most easy-to-obtain weak supervision, i.e., trending topic or hashtag. Thus, this framework can be easily integrated into social media platforms such as Twitter to generate sequential summaries for the events of interest. We also make the manually generated gold-standard sequential summaries of the two test events publicly available for future use in the community.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Item-Item Music Recommendations With Side Information
Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of…
Collaborative FilteringDiversityPrincipled Context Engineering for RAG: Statistical Guarantees via Conformal Prediction
Retrieval-Augmented Generation (RAG) enhances factual grounding in large language models (LLMs) by incorporating retrieved evidence, but LLM accuracy declines when long or noisy contexts exceed the model's effective atte…
Tutorial Recommendation for Livestream Videos using Discourse-Level Consistency and Ontology-Based Filtering
Streaming videos is one of the methods for creators to share their creative works with their audience. In these videos, the streamer share how they achieve their final objective by using various tools in one or several p…
TRUST: Token-dRiven Ultrasound Style Transfer for Cross-Device Adaptation
Ultrasound images acquired from different devices exhibit diverse styles, resulting in decreased performance of downstream tasks. To mitigate the style gap, unpaired image-to-image (UI2I) translation methods aim to trans…
Style TransferE2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation
Retrieval-augmented generation methods often neglect the quality of content retrieved from external knowledge bases, resulting in irrelevant information or potential misinformation that negatively affects the generation …
MisinformationRetrievalRetrieval-augmented GenerationText Generation