| 000 | 05000nam a2201225Ia 4500 | ||
|---|---|---|---|
| 000 | 02796ntm a2200181 i 4500 | ||
| 001 | 90696 | ||
| 003 | 0 | ||
| 005 | 20251017140608.0 | ||
| 008 | 240717n 000 0 eng d | ||
| 010 |
_z _z _o _a _b |
||
| 015 |
_22 _a |
||
| 016 |
_2 _2 _a _z |
||
| 020 |
_e _e _a _b _z _c _q _x |
||
| 022 |
_y _y _l _a2 |
||
| 024 |
_2 _2 _d _c _a _q |
||
| 028 |
_a _a _b |
||
| 029 |
_a _a _b |
||
| 032 |
_a _a _b |
||
| 035 |
_a _a _b _z _c _q |
||
| 037 |
_n _n _c _a _b |
||
| 040 |
_e _erda _a _d _b _c |
||
| 041 |
_e _e _a _b _g _h _r |
||
| 043 |
_a _a _b |
||
| 045 |
_b _b _a |
||
| 050 |
_a _a _d _b2 _c0 |
||
| 051 |
_c _c _a _b |
||
| 055 |
_a _a _b |
||
| 060 |
_a _a _b |
||
| 070 |
_a _a _b |
||
| 072 |
_2 _2 _d _a _x |
||
| 082 |
_a _a _d _b2 _c |
||
| 084 |
_2 _2 _a |
||
| 086 |
_2 _2 _a |
||
| 090 |
_a _a _m _b _q |
||
| 092 |
_f _f _a _b |
||
| 096 |
_a _a _b |
||
| 097 |
_a _a _b |
||
| 100 |
_e _e _aAlter C. Orbino Jr. Seth Gabriel F. Payumo. _d _b4 _u _c0 _q16 |
||
| 110 |
_e _e _a _d _b _n _c _k |
||
| 111 |
_a _a _d _b _n _c |
||
| 130 |
_s _s _a _p _f _l _k |
||
| 210 |
_a _a _b |
||
| 222 |
_a _a _b |
||
| 240 |
_s _s _a _m _g _n _f _l _o _p _k |
||
| 245 | 0 |
_a _aAn Enhancement of Glove algorithm for extractive text summarization of news articles. _d _b _n _c _h6 _p |
|
| 246 |
_a _a _b _n _i _f6 _p |
||
| 249 |
_i _i _a |
||
| 250 |
_6 _6 _a _b |
||
| 260 |
_e _e _a _b _f _c _g |
||
| 264 |
_3 _3 _a _d _b _c46 |
||
| 300 |
_e _e _c _a _b |
||
| 310 |
_a _a _b |
||
| 321 |
_a _a _b |
||
| 336 |
_b _atext _2rdacontent |
||
| 337 |
_3 _30 _b _aunmediated _2rdamedia |
||
| 338 |
_3 _30 _b _avolume _2rdacarrier |
||
| 340 |
_2 _20 _g _n |
||
| 344 |
_2 _2 _a0 _b |
||
| 347 |
_2 _2 _a0 |
||
| 362 |
_a _a _b |
||
| 385 |
_m _m _a2 |
||
| 410 |
_t _t _b _a _v |
||
| 440 |
_p _p _a _x _v |
||
| 490 |
_a _a _x _v |
||
| 500 |
_a _aUndergraduate Thesis : (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024. _d _b _c56 |
||
| 504 |
_a _a _x |
||
| 505 |
_a _a _b _t _g _r |
||
| 506 |
_a _a5 |
||
| 510 |
_a _a _x |
||
| 520 |
_b _b _c _aABSTRACT This research study addresses the limitations of the GloVe (Global Vectors for Word Representation) algorithm in the context of extractive text summarization, focusing on mitigating sparsity issues and enhancing contextual representation. The study begins by identifying three key challenges faced by GloVe: sparsity issues impacting representation accuracy, high computational demands, and difficulties in capturing contextual semantics. Firstly, the study introduces methods to mitigate sparsity issues in GloVe embeddings. By focusing on handling out-of-vocabulary words and improving the representation of rare terms, the algorithm aims to provide a more comprehensive representation of the entire corpus. Techniques such as subword embeddings and adaptive learning rates employed to address this challenge effectively. Secondly, to address the computational intensity and memory requirements of GloVe, the thesis incorporates dimensionality reduction techniques, specifically t-distributed Stochastic Neighbor Embedding (t-SNE). By reducing the dimensionality of the word embeddings, the algorithm achieves significant improvements in processing speed and memory efficiency without sacrificing summarization quality. Finally, the study enhances the contextual representation learned by GloVe for text summarization purposes. By weighing rare words and giving them better representation, the algorithm improves its ability to capture contextual semantics, thereby enhancing the quality of extractive text summarization. Through a series of experiments and evaluations using real-world news article datasets, the proposed enhancements are thoroughly validated. The results demonstrate notable improvements in the summarization performance of the enhanced GloVe algorithm compared to traditional GloVe implementations. The findings of this thesis contribute to advancing the state-of-the-art in extractive text summarization techniques and provice valuable insights for future research in natural language processing and machine learning. _u |
||
| 521 |
_a _a _b |
||
| 533 |
_e _e _a _d _b _n _c |
||
| 540 |
_c _c _a5 |
||
| 542 |
_g _g _f |
||
| 546 |
_a _a _b |
||
| 583 |
_5 _5 _k _c _a _b |
||
| 590 |
_a _a _b |
||
| 600 |
_b _b _v _t _c2 _q _a _x0 _z _d _y |
||
| 610 |
_b _b _v _t2 _x _a _k0 _p _z _d6 _y |
||
| 611 |
_a _a _d _n2 _c0 _v |
||
| 630 |
_x _x _a _d _p20 _v |
||
| 648 |
_2 _2 _a |
||
| 650 |
_x _x _a _d _b _z _y20 _v |
||
| 651 |
_x _x _a _y20 _v _z |
||
| 655 |
_0 _0 _a _y2 _z |
||
| 700 |
_i _i _t _c _b _s1 _q _f _k40 _p _d _e _a _l _n6 |
||
| 710 |
_b _b _t _c _e _f _k40 _p _d5 _l _n6 _a |
||
| 711 |
_a _a _d _b _n _t _c |
||
| 730 |
_s _s _a _d _n _p _f _l _k |
||
| 740 |
_e _e _a _d _b _n _c6 |
||
| 753 |
_c _c _a |
||
| 767 |
_t _t _w |
||
| 770 |
_t _t _w _x |
||
| 773 |
_a _a _d _g _m _t _b _v _i _p |
||
| 775 |
_t _t _w _x |
||
| 776 |
_s _s _a _d _b _z _i _t _x _h _c _w |
||
| 780 |
_x _x _a _g _t _w |
||
| 785 |
_t _t _w _a _x |
||
| 787 |
_x _x _d _g _i _t _w |
||
| 800 |
_a _a _d _l _f _t0 _q _v |
||
| 810 |
_a _a _b _f _t _q _v |
||
| 830 |
_x _x _a _p _n _l0 _v |
||
| 942 |
_a _alcc _cBK _01 |
||
| 999 |
_c21668 _d21668 |
||