Information　explosion,　both　in　cyberspace　world　and　real　world,　nowadays　has　brought　about　pressing　needs　for　comprehensive　summary　of　information.　The　challenge　for　constructing　a　quality　one　lies　in　filtering　out　information　of　low　relevance　and　mining　out　highly　sparse　relevant　topics　in　the　vast　sea　of　data.　It　is　a　typical　imbalanced　learning　task　and　we　need　to　achieve　a　precise　summary　of　temporal　event　via　an　accurate　description　and　definition　of　the　useful　information　and　redundant　information.　In　response　to　such　challenge,　we　introduced:　(1)　a　uniform　framework　of　temporal　event　summarization　with　minimal　residual　optimization　matrix　factorization　as　its　key　part;　and　(2)　a　novel　neighborhood　preserving　semantic　measure　(NPS)　to　capture　the　sparse　candidate　topics　under　that　low-rank　matrix　factorization　model.　To　evaluate　the　effectiveness　of　the　proposed　solution,　a　series　of　experiments　are　conducted　on　an　annotated　KBA　corpus.　The　results　of　these　experiments　show　that　the　solution　proposed　in　this　study　can　improve　the　quality　of　temporal　summarization　as　compared　with　the　established　baselines.　©　2020　IEEE.