File size: 88,668 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 |
{
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:33:53.646702Z"
},
"title": "A Proposal: Interactively Learning to Summarise Timelines by Reinforcement Learning",
"authors": [
{
"first": "Yuxuan",
"middle": [],
"last": "Ye",
"suffix": "",
"affiliation": {
"laboratory": "Intelligent Systems Laboratory",
"institution": "University of Bristol",
"location": {}
},
"email": "yuxuan.ye@bristol.ac.uk"
},
{
"first": "Edwin",
"middle": [],
"last": "Simpson",
"suffix": "",
"affiliation": {
"laboratory": "Intelligent Systems Laboratory",
"institution": "University of Bristol",
"location": {}
},
"email": "edwin.simpson@bristol.ac.uk"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Timeline Summarisation (TLS) aims to generate a concise, time-ordered list of events described in sources such as news articles. However, current systems do not provide an adequate way to adapt to new domains nor to focus on the aspects of interest to a particular user. Therefore, we propose a method for interactively learning abstractive TLS using Reinforcement Learning (RL). We define a compound reward function and use RL to finetune an abstractive Multi-document Summarisation (MDS) model, which avoids the need to train using reference summaries. One of the sub-reward functions will be learned interactively from user feedback to ensure the consistency between users' demands and the generated timeline. The other sub-reward functions contribute to topical coherence and linguistic fluency. We plan experiments to evaluate whether our approach could generate accurate and precise timelines tailored for each user.",
"pdf_parse": {
"paper_id": "2021",
"_pdf_hash": "",
"abstract": [
{
"text": "Timeline Summarisation (TLS) aims to generate a concise, time-ordered list of events described in sources such as news articles. However, current systems do not provide an adequate way to adapt to new domains nor to focus on the aspects of interest to a particular user. Therefore, we propose a method for interactively learning abstractive TLS using Reinforcement Learning (RL). We define a compound reward function and use RL to finetune an abstractive Multi-document Summarisation (MDS) model, which avoids the need to train using reference summaries. One of the sub-reward functions will be learned interactively from user feedback to ensure the consistency between users' demands and the generated timeline. The other sub-reward functions contribute to topical coherence and linguistic fluency. We plan experiments to evaluate whether our approach could generate accurate and precise timelines tailored for each user.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Notable events often happen over a long period. For example, COVID-19 caused immeasurable damage around the world, lasting for more than a year. When reviewing different aspects of the disaster, the huge number of reports and news articles makes it difficult to trace the development of events such as outbreaks, policy interventions and vaccination efforts. TLS can solve this problem by identifying significant dates and summarising events of sub-topics.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Most prior TLS works focused on producing extractive timelines, which copies the original sentences from input documents (Martschat and Markert, 2018; Nguyen et al., 2014; Yan et al., 2011) . Irrelevant and repeated information may be extracted in this process, decreasing the quality of the generated timelines. Abstractive timeline summari-sation methods can address this problem (Steen and Markert, 2019; Barros et al., 2019) but few neural network models have been proposed due to the lack of reference timelines for supervised learning. Producing reference timelines by human requires expertise to capture important temporal information and sub-events from the source documents, thus it is extremely expensive. In MDS tasks, researchers have tried heuristics-based and unsupervised methods to address the reference data shortage problem (Ryang and Abekawa, 2012; Rioux et al., 2014) . However, their results on some evaluation metrics, like ROUGE-2, only reached half of the upper bound. Gao et al. (2018) showed that interactive learning could improve the performance of an MDS system via leveraging users' preference, which is relatively easy to obtain, and does not require reference summaries. Therefore, we take inspiration from their work to propose an interaction-based abstractive TLS framework.",
"cite_spans": [
{
"start": 121,
"end": 150,
"text": "(Martschat and Markert, 2018;",
"ref_id": "BIBREF14"
},
{
"start": 151,
"end": 171,
"text": "Nguyen et al., 2014;",
"ref_id": "BIBREF18"
},
{
"start": 172,
"end": 189,
"text": "Yan et al., 2011)",
"ref_id": "BIBREF30"
},
{
"start": 382,
"end": 407,
"text": "(Steen and Markert, 2019;",
"ref_id": "BIBREF25"
},
{
"start": 408,
"end": 428,
"text": "Barros et al., 2019)",
"ref_id": "BIBREF0"
},
{
"start": 842,
"end": 867,
"text": "(Ryang and Abekawa, 2012;",
"ref_id": "BIBREF21"
},
{
"start": 868,
"end": 887,
"text": "Rioux et al., 2014)",
"ref_id": "BIBREF20"
},
{
"start": 993,
"end": 1010,
"text": "Gao et al. (2018)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Martschat and Markert (2018) treated the TLS task as an MDS task and proposed a modular summarisation method, which achieved the state of the art and is adaptable. However, its adaptation requires abstracting mathematical constraints from concrete requirements. This contrasts with interactive learning (IL), which greatly decreases the cognitive burden for humans by receiving user feedback to refine summaries (Gao et al., 2018; Lin et al., 2010) . Comparing to traditional approaches, interaction enables the model to learn from the users, thus it is possible to accurately tailor and refine timeline summaries according to users' demands.",
"cite_spans": [
{
"start": 412,
"end": 430,
"text": "(Gao et al., 2018;",
"ref_id": "BIBREF5"
},
{
"start": 431,
"end": 448,
"text": "Lin et al., 2010)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we propose an interaction-based abstractive timeline summarisation framework using deep RL. By learning a reward signal from user feedback, we can fine-tune a pretrained MDS model for the TLS task via a small number of interactive learning rounds. Therefore, our frame- work should be capable of generating timeline summaries with high text quality after enough episodes of training. And we plan both simulation and realuser experiments to evaluate the framework on two benchmark TLS datasets, Timeline17 and Crisis (Tran et al., 2015) .",
"cite_spans": [
{
"start": 531,
"end": 550,
"text": "(Tran et al., 2015)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The workflow of our model ( Figure 1 ) mainly follows the event detection method, CLUST (Ghalandari and Ifrim, 2020), which identifies subevents first and then generates summaries for them. Due to the RL-based interactive learning process in the framework, our model can be automatically adapted to new topics and adjusted by users' interests.",
"cite_spans": [],
"ref_spans": [
{
"start": 28,
"end": 36,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1. Firstly, we embed source documents into vectors and cluster them in vector space. Each cluster represents a sub-event in a large topic;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "2. In the next step, we assign a date to each cluster. And they will be ranked by a metric to identify important sub-events;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "3. Then it comes to our RL-based interactive learning process.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(a) An abstractive MDS model will generate summaries for each sub-event. All summaries will be ordered by date to form a timeline. (b) The user can preview the timeline in this step and respond by expressing prefer-ences over keywords or by comparing the new summary to an earlier version. (c) Using a reward function that evaluates the consistency between the produced timeline and those user preferences, offline RL then tunes the model and starts another round of interactive learning.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our main contribution is a proposed interactive method for generating timelines for news, which adapts to user feedback through RL fine-tuning.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Extractive Timeline Summarisation Prior extractive methods (Martschat and Markert, 2018; Ghalandari, 2017) defined several objective functions to assess the quality of timelines, including coverage of summaries and temporal information. These methods greedily select one sentence in each iteration to maximise the combined objective function. Our reward function is also modular but lacks monotonicity and submodularity, hence we use RL instead of a greedy algorithm.",
"cite_spans": [
{
"start": 59,
"end": 88,
"text": "(Martschat and Markert, 2018;",
"ref_id": "BIBREF14"
},
{
"start": 89,
"end": 106,
"text": "Ghalandari, 2017)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Interactive Summarisation Instead of producing reference texts by crowdsourcing, obtaining information (e.g., keywords) via user interaction can be more practical to obtain training data. Liu et al. (2012) outperformed previous extractive MDS approaches on ROUGE-based metrics by querying topic words from users. Gao et al. (2018) collected pairwise comparisons between summaries from simulated users, which are then used to train a ranker without any reference data, and fixed the efficiency issue of IL. Due to the similarity between the MDS and TLS task, IL is expected to solve the reference timelines shortage problem as well, without increasing many computation expenses. So we introduce interaction into an RL-based TLS model for the first time.",
"cite_spans": [
{
"start": 188,
"end": 205,
"text": "Liu et al. (2012)",
"ref_id": "BIBREF11"
},
{
"start": 313,
"end": 330,
"text": "Gao et al. (2018)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Reinforcement Learning in Natural Language Generation (NLG) Recent research on applying RL on NLG tasks has received some success. Some prior works on dialogue systems (Song et al., 2020; Mesgar et al., 2020) utilized RL-based fine-tuning method to ensure the factual consistency of the response. In automatic summarisation (Gao et al., 2018 , IL is applied to learn a reward function from users, so that RL agents could learn a policy to summarise text indirectly under users' guidance. However, for the TLS task, we are the first to use RL to generate summaries for key dates.",
"cite_spans": [
{
"start": 168,
"end": 187,
"text": "(Song et al., 2020;",
"ref_id": "BIBREF24"
},
{
"start": 188,
"end": 208,
"text": "Mesgar et al., 2020)",
"ref_id": "BIBREF15"
},
{
"start": 324,
"end": 341,
"text": "(Gao et al., 2018",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "All components of our method shown in Figure 1 will be introduced below.",
"cite_spans": [],
"ref_spans": [
{
"start": 38,
"end": 46,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Method",
"sec_num": "3"
},
{
"text": "Clustering For each input document, we use the sentence-transformer (Reimers and Gurevych, 2019) based on DistilRoBERTa (Liu et al., 2019) to embed its sentences. Then we represent the document by the mean of the sentence vectors expecting that dense vectors could capture more information in text than TF-IDF vectors, as used in Steen and Markert (2019) and Ghalandari and Ifrim (2020) .",
"cite_spans": [
{
"start": 120,
"end": 138,
"text": "(Liu et al., 2019)",
"ref_id": "BIBREF12"
},
{
"start": 330,
"end": 354,
"text": "Steen and Markert (2019)",
"ref_id": "BIBREF25"
},
{
"start": 359,
"end": 386,
"text": "Ghalandari and Ifrim (2020)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "Next, we use Affinity Propagation (AP) (Frey and Dueck, 2007) to cluster all the documents. AP is an unsupervised method, which automatically determines the number of clusters. AP uses an affinity matrix A, constructed by the Euclidean distance of each pair of document vectors.",
"cite_spans": [
{
"start": 39,
"end": 61,
"text": "(Frey and Dueck, 2007)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "To detect events accurately, we add constraints to the clustering algorithm. If two reports were published too apart from each other, although, with a small distance in vector space, they should be considered to belong to two similar but different sub-topics. In our model, we keep the setting of prior work (Steen and Markert, 2019 ). If d i and d j were published no more than t day(s) apart,",
"cite_spans": [
{
"start": 308,
"end": 332,
"text": "(Steen and Markert, 2019",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "A i,j = \u2212 d i \u2212 d j 1/2",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "Date Assignment By clustering all the documents, reports describing the same event are gathered. However, temporal information is equally as important as summaries in TLS, which differs from MDS. Martschat and Markert (2018) and adapted MDS methods to make them temporally sensitive. Both received outstanding results. In our work, we use HeidelTime (Str\u00f6tgen and Gertz, 2015) to identify and count date expressions in documents. Following Ghalandari and Ifrim (2020), we assign each cluster with the most frequently mentioned date in it.",
"cite_spans": [
{
"start": 196,
"end": 224,
"text": "Martschat and Markert (2018)",
"ref_id": "BIBREF14"
},
{
"start": 350,
"end": 376,
"text": "(Str\u00f6tgen and Gertz, 2015)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "Cluster Ranking Some clusters contain less important information than others. According to Ghalandari and Ifrim (2020), the importance of a cluster is in proportion to the number of sentences that mentions the assigned date to some extent. To capture useful information, we use the same setting and only summarise the top-k important clusters.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "Cluster Summarisation & Timeline Construction Summarising the sub-topic of a key date can be regarded as an MDS task, as each event has multiple sources. We plan to fine-tune an abstractive MDS model for this task, which will be introduced later. After all the top-k clusters are summarised, we combine all the summaries by date to generate a timeline. We follow the setting of Ghalandari and Ifrim (2020), which skips a cluster when its date is already used by another prior cluster. Every time the timeline is generated, the user can preview it and provide several types of feedback such as keywords and dates that must be included or excluded, and expressing preferences against previous version of the timeline. Given these feedback, we can renew our reward function and finetune the summariser via hundreds of RL episodes. Then we can produce a new timeline to start another round of interactive learning. After several interactive learning rounds, our model would be able to generate and tailor a high-quality timeline for the user.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Detection Timeline Summarisation",
"sec_num": "3.1"
},
{
"text": "AR-F1 AR-F2 CLUST 0.082 0.02 PEGASUS-Multi News 0.089 0.019 PEGASUS We use PEGASUS to solve the MDS task on each cluster. PE-GASUS is an abstractive summariser providing various fine-tuned versions. PEGASUS-Multi News is fine-tuned on Multi-News (Fabbri et al., 2019) to summarise news articles. We found that PEGASUS-Multi News outperforms the state-ofthe-art extractive event detection method, CLUST (Ghalandari and Ifrim, 2020), when applying it directly on clusters without fine-tuning (Table 1) . Therefore, it provides a strong basis for our following work. PEGASUS-RL Although PEGASUS is powerful enough to generate high-quality summaries, we still need RL to ensure the summaries are topically coherent and linguistically fluent. The PEGASUS model generates summaries token-by-token. When the last token, i.e. eos , is generated, the reward component will assess the quality of the summary and produce a reward signal to update the summarising policy (Figure 3 ). This whole process will tune the parameters of PEGASUS so that it enhances the quality of the generated summary as well.",
"cite_spans": [
{
"start": 235,
"end": 267,
"text": "Multi-News (Fabbri et al., 2019)",
"ref_id": null
}
],
"ref_spans": [
{
"start": 490,
"end": 499,
"text": "(Table 1)",
"ref_id": "TABREF0"
},
{
"start": 959,
"end": 968,
"text": "(Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "Action and Reward Function Let D = (d 1 , d individually. And S = (t 1 , t 2 , . . . , t |S| ) is the summary generated for cluster D. Our goal is to finetune a single model to generate a summary S, for each cluster D that is linguistically fluent and topi-cally coherent with any d i and consistent with any piece of feedback p i , m i , n i . We regard each token generation process in Figure 3 as an action of PEGASUS. Our model is expected to generate a summary with topical coherence, linguistic fluency and consistency with the user's demands for each cluster. Thus, a compound reward function is proposed, which consists of four sub-reward functions: R 1 guarantees topical coherence with the cluster, R 2 enforces consistency with each piece of individual user feedback, R 3 and R 4 contribute to the linguistic fluency of the produced summaries. The reward of the cluster D is the weighted sum of them.",
"cite_spans": [],
"ref_spans": [
{
"start": 35,
"end": 43,
"text": "(d 1 , d",
"ref_id": "FIGREF0"
},
{
"start": 388,
"end": 396,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "R C = \u03b3 1 R 1 + \u03b3 2 R 2 + \u03b3 2 R 3 + \u03b3 4 R 4 (1)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "where \u03b3 1,2,3,4 are the normalization factors that sum to one. The whole training signal R is the sum of k selected clusters' rewards.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "Topical coherence sub-reward (R 1 and R 2 ) Topical coherence is the pivotal property of a summary. We measure how topically coherent the summary S is with a cluster D by their cosine similarity.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "R 1 = cos ( S, D)",
"eq_num": "(2)"
}
],
"section": "Timeline17",
"sec_num": null
},
{
"text": "R 2 is the core reward function in the fine-tuning process, which will be updated in each interactive learning round. We embed all the keywords in M and N to dense vectors and measure their topic coherence by cosine similarities. Due to N represents the words that the user wants to exclude, we set its reward to be negative. To accommodate pairwise preference labels, we learn a ranking function using a random utility model (Thurstone, 1927; Mosteller, 2006) . This provides a scoring function that should also be added to R 2 .",
"cite_spans": [
{
"start": 426,
"end": 443,
"text": "(Thurstone, 1927;",
"ref_id": "BIBREF27"
},
{
"start": 444,
"end": 460,
"text": "Mosteller, 2006)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "R 2 = w 1 score( S, P ) + w 2 m i \u2208M cos ( S, m i ) \u2212 w 3 n i \u2208N cos ( S, n i ) (3)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "where w 1,2,3 are the normalization factors.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "Linguistic fluency sub-reward (R 3 and R 4 ) Prior work (Mesgar et al., 2020) has shown that applying RL to improve evaluation metrics' results might lead to decreasing in linguistic quality. To avoid that, we apply two sub-reward functions to our model. R 3 utilizes a language model which has been fine-tuned on a similar news dataset:",
"cite_spans": [
{
"start": 56,
"end": 77,
"text": "(Mesgar et al., 2020)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "R 3 = \u03b1 \u2212 N (S) \u03b1 (4)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "where N (\u2022) is the Negative Log-likelihood loss function, and \u03b1 is the maximum of N (\u2022) so that it can normalize R 3 . R 4 reduces repeated words in summaries, by penalizing repeated unigrams:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "R 4 = 1 \u2212 #repeated tokens in summary #tokens in summary (5)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "Training In this work, RL attempts to learn a policy P \u03b8 that generates a summary maximizing the expectation of the reward function.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "L = E S\u223cP \u03b8 [R(S, (C, F ))]",
"eq_num": "(6)"
}
],
"section": "Timeline17",
"sec_num": null
},
{
"text": "However, RL is known for high variance issue when sampling the gradient. To solve this problem, we plan to run several hundred episodes of RL to increase the size of the sample and reduce the variance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "In addition, according to Mnih et al. (2016) and Mesgar et al. (2020) , we can tune the policy function by actor-critic, which could further reduce variance in learning. In actor-critic algorithm, the policy function P \u03b8 is regarded as the actor, and we define the residual of temporal difference \u03a8 t to be the critic. Although \u03a8 t is a biased estimation of the reward function R, we can reduce the variance via replacing the reward function R in the policy gradient equation (7) by \u03a8 t , as in the following:",
"cite_spans": [
{
"start": 26,
"end": 44,
"text": "Mnih et al. (2016)",
"ref_id": "BIBREF16"
},
{
"start": 49,
"end": 69,
"text": "Mesgar et al. (2020)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "g = E t=0 \u03a8 t \u2207 \u03b8 log P \u03b8 (a t |s t )",
"eq_num": "(7)"
}
],
"section": "Timeline17",
"sec_num": null
},
{
"text": "4 Plan for Evaluation",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "As a kind of summarisation task, correctly extracting temporal information is the special challenge of TLS, which makes the evaluation more complex as well. In our work, we plan to evaluate our model by the suitable evaluation metrics proposed by Martschat and Markert (2017) .",
"cite_spans": [
{
"start": 247,
"end": 275,
"text": "Martschat and Markert (2017)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "Concatenation ROUGE Discard all dates and concatenate all summaries in the reference and the output timeline. Evaluate ROUGE on two concatenated texts.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "Alignment ROUGE Align the output timeline with the reference by the similarity and distance of their dates and apply ROUGE on them. Aligned summaries with distant dates will be penalized. User feedback will be generated through mixed simulations, as in and studies with real users. Simulations will rely on references, from which keywords and dates can be extracted. Pairwise preferences can be simulated by comparing two summaries to a reference using ROUGE and selecting the highest-scoring summary. The system will be tested with different feedback types (keywords, dates, inclusion/exclusion, and preferences) to determine whether these forms of interaction are feasible to improve the summaries. However, the simulated user labels will be noisy, so we intend to evaluate with real users once we have developed a working system.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Timeline17",
"sec_num": null
},
{
"text": "We propose an interactive method to summarise timelines without reference data. In each interactive learning round, we first update the reward function, and then use RL to fine-tune a huge neural network model. Then the model will generate summaries for each of the important sub-events, which are identified by textual similarity to the articles in the corpus. All the summaries will be ordered by their assigned dates to form a timeline. The user can preview the timeline and give feedback to start another round of interactive learning. Part of our method has been implemented, including PEGA-SUS to summarise event clusters but without RL or user feedback. Given the current experiment results, we can expect better performance after the interaction part implemented. The challenge remains in RL and designing suitable modes of interaction. We will move forward to our planned experiments and report our results in future work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Summary",
"sec_num": "5"
},
{
"text": ", otherwise it will be assigned by 0.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Natsum: Narrative abstractive summarization through cross-document timeline generation",
"authors": [
{
"first": "Cristina",
"middle": [],
"last": "Barros",
"suffix": ""
},
{
"first": "Elena",
"middle": [],
"last": "Lloret",
"suffix": ""
},
{
"first": "Estela",
"middle": [],
"last": "Saquete",
"suffix": ""
},
{
"first": "Borja",
"middle": [],
"last": "Navarro-Colorado",
"suffix": ""
}
],
"year": 2019,
"venue": "Information Processing & Management",
"volume": "56",
"issue": "5",
"pages": "1775--1793",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Cristina Barros, Elena Lloret, Estela Saquete, and Borja Navarro-Colorado. 2019. Natsum: Narrative abstractive summarization through cross-document timeline generation. Information Processing & Management, 56(5):1775-1793.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Predicting relevant news events for timeline summaries",
"authors": [
{
"first": "Mohammad",
"middle": [],
"last": "Giang Binh Tran",
"suffix": ""
},
{
"first": "Dat Quoc",
"middle": [],
"last": "Alrifai",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Nguyen",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 22nd International Conference on World Wide Web",
"volume": "",
"issue": "",
"pages": "91--92",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Giang Binh Tran, Mohammad Alrifai, and Dat Quoc Nguyen. 2013. Predicting relevant news events for timeline summaries. In Proceedings of the 22nd International Conference on World Wide Web, pages 91-92.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Learning towards abstractive timeline summarization",
"authors": [
{
"first": "Xiuying",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Zhangming",
"middle": [],
"last": "Chan",
"suffix": ""
},
{
"first": "Shen",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Meng-Hsuan",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Dongyan",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Rui",
"middle": [],
"last": "Yan",
"suffix": ""
}
],
"year": 2019,
"venue": "IJCAI",
"volume": "",
"issue": "",
"pages": "4939--4945",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiuying Chen, Zhangming Chan, Shen Gao, Meng- Hsuan Yu, Dongyan Zhao, and Rui Yan. 2019. Learning towards abstractive timeline summariza- tion. In IJCAI, pages 4939-4945.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model",
"authors": [
{
"first": "Irene",
"middle": [],
"last": "Alexander R Fabbri",
"suffix": ""
},
{
"first": "Tianwei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Suyi",
"middle": [],
"last": "She",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Dragomir R Radev",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1906.01749"
]
},
"num": null,
"urls": [],
"raw_text": "Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. arXiv preprint arXiv:1906.01749.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Clustering by passing messages between data points. science",
"authors": [
{
"first": "J",
"middle": [],
"last": "Brendan",
"suffix": ""
},
{
"first": "Delbert",
"middle": [],
"last": "Frey",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Dueck",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "315",
"issue": "",
"pages": "972--976",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Brendan J Frey and Delbert Dueck. 2007. Clustering by passing messages between data points. science, 315(5814):972-976.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "April: Interactively learning to summarise by combining active preference learning and reinforcement learning",
"authors": [
{
"first": "Yang",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Christian",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Meyer",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1808.09658"
]
},
"num": null,
"urls": [],
"raw_text": "Yang Gao, Christian M Meyer, and Iryna Gurevych. 2018. April: Interactively learning to summarise by combining active preference learning and reinforce- ment learning. arXiv preprint arXiv:1808.09658.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Reward learning for efficient reinforcement learning in extractive document summarisation",
"authors": [
{
"first": "Yang",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Christian",
"suffix": ""
},
{
"first": "Mohsen",
"middle": [],
"last": "Meyer",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Mesgar",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1907.12894"
]
},
"num": null,
"urls": [],
"raw_text": "Yang Gao, Christian M Meyer, Mohsen Mesgar, and Iryna Gurevych. 2019. Reward learning for efficient reinforcement learning in extractive document sum- marisation. arXiv preprint arXiv:1907.12894.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Revisiting the centroid-based method: A strong baseline for multi-document summarization",
"authors": [
{
"first": "Ghalandari",
"middle": [],
"last": "Demian Gholipour",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1708.07690"
]
},
"num": null,
"urls": [],
"raw_text": "Demian Gholipour Ghalandari. 2017. Revisiting the centroid-based method: A strong baseline for multi-document summarization. arXiv preprint arXiv:1708.07690.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Examining the state-of-the-art in news timeline summarization",
"authors": [
{
"first": "Gholipour",
"middle": [],
"last": "Demian",
"suffix": ""
},
{
"first": "Georgiana",
"middle": [],
"last": "Ghalandari",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ifrim",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2005.10107"
]
},
"num": null,
"urls": [],
"raw_text": "Demian Gholipour Ghalandari and Georgiana Ifrim. 2020. Examining the state-of-the-art in news timeline summarization. arXiv preprint arXiv:2005.10107.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Putting the user in the loop: interactive maximal marginal relevance for query-focused summarization",
"authors": [
{
"first": "Jimmy",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Nitin",
"middle": [],
"last": "Madnani",
"suffix": ""
},
{
"first": "Bonnie",
"middle": [],
"last": "Dorr",
"suffix": ""
}
],
"year": 2010,
"venue": "Human Language Technologies: The",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jimmy Lin, Nitin Madnani, and Bonnie Dorr. 2010. Putting the user in the loop: interactive maximal marginal relevance for query-focused summariza- tion. In Human Language Technologies: The 2010",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Annual Conference of the North American Chapter of the Association for Computational Linguistics",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "305--308",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Conference of the North American Chap- ter of the Association for Computational Linguistics, pages 305-308.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Query-oriented multi-document summarization via unsupervised deep learning",
"authors": [
{
"first": "Yan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Sheng-Hua",
"middle": [],
"last": "Zhong",
"suffix": ""
},
{
"first": "Wenjie",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yan Liu, Sheng-hua Zhong, and Wenjie Li. 2012. Query-oriented multi-document summarization via unsupervised deep learning. In Proceedings of the AAAI Conference on Artificial Intelligence, vol- ume 26.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Roberta: A robustly optimized bert pretraining approach",
"authors": [
{
"first": "Yinhan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Myle",
"middle": [],
"last": "Ott",
"suffix": ""
},
{
"first": "Naman",
"middle": [],
"last": "Goyal",
"suffix": ""
},
{
"first": "Jingfei",
"middle": [],
"last": "Du",
"suffix": ""
},
{
"first": "Mandar",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Danqi",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Mike",
"middle": [],
"last": "Lewis",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Veselin",
"middle": [],
"last": "Stoyanov",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1907.11692"
]
},
"num": null,
"urls": [],
"raw_text": "Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint arXiv:1907.11692.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Improving rouge for timeline summarization",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Martschat",
"suffix": ""
},
{
"first": "Katja",
"middle": [],
"last": "Markert",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "285--290",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sebastian Martschat and Katja Markert. 2017. Improv- ing rouge for timeline summarization. In Proceed- ings of the 15th Conference of the European Chap- ter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 285-290.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "A temporally sensitive submodularity framework for timeline summarization",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Martschat",
"suffix": ""
},
{
"first": "Katja",
"middle": [],
"last": "Markert",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1810.07949"
]
},
"num": null,
"urls": [],
"raw_text": "Sebastian Martschat and Katja Markert. 2018. A temporally sensitive submodularity framework for timeline summarization. arXiv preprint arXiv:1810.07949.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Generating persona-consistent dialogue responses using deep reinforcement learning",
"authors": [
{
"first": "Mohsen",
"middle": [],
"last": "Mesgar",
"suffix": ""
},
{
"first": "Edwin",
"middle": [],
"last": "Simpson",
"suffix": ""
},
{
"first": "Yue",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2005.00036"
]
},
"num": null,
"urls": [],
"raw_text": "Mohsen Mesgar, Edwin Simpson, Yue Wang, and Iryna Gurevych. 2020. Generating persona-consistent di- alogue responses using deep reinforcement learning. arXiv preprint arXiv:2005.00036.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Asynchronous methods for deep reinforcement learning",
"authors": [
{
"first": "Volodymyr",
"middle": [],
"last": "Mnih",
"suffix": ""
},
{
"first": "Adria",
"middle": [
"Puigdomenech"
],
"last": "Badia",
"suffix": ""
},
{
"first": "Mehdi",
"middle": [],
"last": "Mirza",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Graves",
"suffix": ""
},
{
"first": "Timothy",
"middle": [],
"last": "Lillicrap",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Harley",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Silver",
"suffix": ""
},
{
"first": "Koray",
"middle": [],
"last": "Kavukcuoglu",
"suffix": ""
}
],
"year": 2016,
"venue": "International conference on machine learning",
"volume": "",
"issue": "",
"pages": "1928--1937",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asyn- chronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928-1937. PMLR.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Remarks on the method of paired comparisons: I. the least squares solution assuming equal standard deviations and equal correlations",
"authors": [
{
"first": "Frederick",
"middle": [],
"last": "Mosteller",
"suffix": ""
}
],
"year": 2006,
"venue": "Selected Papers of Frederick Mosteller",
"volume": "",
"issue": "",
"pages": "157--162",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Frederick Mosteller. 2006. Remarks on the method of paired comparisons: I. the least squares solution as- suming equal standard deviations and equal corre- lations. In Selected Papers of Frederick Mosteller, pages 157-162. Springer.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Ranking multidocument event descriptions for building thematic timelines",
"authors": [
{
"first": "Kiem-Hieu",
"middle": [],
"last": "Nguyen",
"suffix": ""
},
{
"first": "Xavier",
"middle": [],
"last": "Tannier",
"suffix": ""
},
{
"first": "V\u00e9ronique",
"middle": [],
"last": "Moriceau",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
"volume": "",
"issue": "",
"pages": "1208--1217",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kiem-Hieu Nguyen, Xavier Tannier, and V\u00e9ronique Moriceau. 2014. Ranking multidocument event de- scriptions for building thematic timelines. In Pro- ceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Techni- cal Papers, pages 1208-1217.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Sentencebert: Sentence embeddings using siamese bertnetworks",
"authors": [
{
"first": "Nils",
"middle": [],
"last": "Reimers",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1908.10084"
]
},
"num": null,
"urls": [],
"raw_text": "Nils Reimers and Iryna Gurevych. 2019. Sentence- bert: Sentence embeddings using siamese bert- networks. arXiv preprint arXiv:1908.10084.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Fear the reaper: A system for automatic multidocument summarization with reinforcement learning",
"authors": [
{
"first": "Cody",
"middle": [],
"last": "Rioux",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Sadid",
"suffix": ""
},
{
"first": "Yllias",
"middle": [],
"last": "Hasan",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Chali",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "681--690",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Cody Rioux, Sadid A Hasan, and Yllias Chali. 2014. Fear the reaper: A system for automatic multi- document summarization with reinforcement learn- ing. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 681-690.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Framework of automatic text summarization using reinforcement learning",
"authors": [
{
"first": "Seonggi",
"middle": [],
"last": "Ryang",
"suffix": ""
},
{
"first": "Takeshi",
"middle": [],
"last": "Abekawa",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Seonggi Ryang and Takeshi Abekawa. 2012. Frame- work of automatic text summarization using rein- forcement learning. In Proceedings of the 2012",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Natural Language Processing and Computational Natural Language Learning",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "256--265",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 256-265.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Interactive text ranking with bayesian optimization: A case study on community qa and summarization",
"authors": [
{
"first": "Edwin",
"middle": [],
"last": "Simpson",
"suffix": ""
},
{
"first": "Yang",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2020,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "8",
"issue": "",
"pages": "759--775",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Edwin Simpson, Yang Gao, and Iryna Gurevych. 2020. Interactive text ranking with bayesian optimization: A case study on community qa and summarization. Transactions of the Association for Computational Linguistics, 8:759-775.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Generating persona consistent dialogues by exploiting natural language inference",
"authors": [
{
"first": "Haoyu",
"middle": [],
"last": "Song",
"suffix": ""
},
{
"first": "Wei-Nan",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Jingwen",
"middle": [],
"last": "Hu",
"suffix": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the AAAI Conference on Artificial Intelligence",
"volume": "34",
"issue": "",
"pages": "8878--8885",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Haoyu Song, Wei-Nan Zhang, Jingwen Hu, and Ting Liu. 2020. Generating persona consistent dialogues by exploiting natural language inference. In Pro- ceedings of the AAAI Conference on Artificial Intel- ligence, volume 34, pages 8878-8885.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Abstractive timeline summarization",
"authors": [
{
"first": "Julius",
"middle": [],
"last": "Steen",
"suffix": ""
},
{
"first": "Katja",
"middle": [],
"last": "Markert",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
"volume": "",
"issue": "",
"pages": "21--31",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julius Steen and Katja Markert. 2019. Abstractive time- line summarization. In Proceedings of the 2nd Work- shop on New Frontiers in Summarization, pages 21- 31.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "A baseline temporal tagger for all languages",
"authors": [
{
"first": "Jannik",
"middle": [],
"last": "Str\u00f6tgen",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Gertz",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 conference on empirical methods in natural language processing",
"volume": "",
"issue": "",
"pages": "541--547",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jannik Str\u00f6tgen and Michael Gertz. 2015. A baseline temporal tagger for all languages. In Proceedings of the 2015 conference on empirical methods in natural language processing, pages 541-547.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "A law of comparative judgment",
"authors": [
{
"first": "",
"middle": [],
"last": "Louis L Thurstone",
"suffix": ""
}
],
"year": 1927,
"venue": "Psychological review",
"volume": "34",
"issue": "4",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Louis L Thurstone. 1927. A law of comparative judg- ment. Psychological review, 34(4):273.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Timeline summarization from relevant headlines",
"authors": [
{
"first": "Giang",
"middle": [],
"last": "Tran",
"suffix": ""
},
{
"first": "Mohammad",
"middle": [],
"last": "Alrifai",
"suffix": ""
},
{
"first": "Eelco",
"middle": [],
"last": "Herder",
"suffix": ""
}
],
"year": 2015,
"venue": "European Conference on Information Retrieval",
"volume": "",
"issue": "",
"pages": "245--256",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Giang Tran, Mohammad Alrifai, and Eelco Herder. 2015. Timeline summarization from relevant head- lines. In European Conference on Information Re- trieval, pages 245-256. Springer.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Leveraging learning to rank in an optimization framework for timeline summarization",
"authors": [
{
"first": "Tuan",
"middle": [
"A"
],
"last": "Giang Binh Tran",
"suffix": ""
},
{
"first": "Nam-Khanh",
"middle": [],
"last": "Tran",
"suffix": ""
},
{
"first": "Mohammad",
"middle": [],
"last": "Tran",
"suffix": ""
},
{
"first": "Nattiya",
"middle": [],
"last": "Alrifai",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Kanhabua",
"suffix": ""
}
],
"year": 2013,
"venue": "SIGIR 2013 Workshop on Time-aware Information Access",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Giang Binh Tran, Tuan A Tran, Nam-Khanh Tran, Mohammad Alrifai, and Nattiya Kanhabua. 2013. Leveraging learning to rank in an optimization framework for timeline summarization. In SIGIR 2013 Workshop on Time-aware Information Access (TAIA.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Evolutionary timeline summarization: a balanced optimization framework via iterative substitution",
"authors": [
{
"first": "Rui",
"middle": [],
"last": "Yan",
"suffix": ""
},
{
"first": "Xiaojun",
"middle": [],
"last": "Wan",
"suffix": ""
},
{
"first": "Jahna",
"middle": [],
"last": "Otterbacher",
"suffix": ""
},
{
"first": "Liang",
"middle": [],
"last": "Kong",
"suffix": ""
},
{
"first": "Xiaoming",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Yan",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval",
"volume": "",
"issue": "",
"pages": "745--754",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rui Yan, Xiaojun Wan, Jahna Otterbacher, Liang Kong, Xiaoming Li, and Yan Zhang. 2011. Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 745-754.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Pegasus: Pre-training with extracted gap-sentences for abstractive summarization",
"authors": [
{
"first": "Jingqing",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Yao",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Mohammad",
"middle": [],
"last": "Saleh",
"suffix": ""
},
{
"first": "Peter",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2020,
"venue": "International Conference on Machine Learning",
"volume": "",
"issue": "",
"pages": "11328--11339",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Pe- ter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In In- ternational Conference on Machine Learning, pages 11328-11339. PMLR.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"uris": null,
"text": "The workflow of our event detection timeline summarisation method",
"num": null
},
"FIGREF1": {
"type_str": "figure",
"uris": null,
"text": "A view of interaction process",
"num": null
},
"FIGREF2": {
"type_str": "figure",
"uris": null,
"text": "A view of our RL method",
"num": null
},
"TABREF0": {
"content": "<table><tr><td>: Performance of two methods evaluated by</td></tr><tr><td>Alignment ROUGE-1 and Alignment ROUGE-2.</td></tr></table>",
"text": "",
"num": null,
"html": null,
"type_str": "table"
},
"TABREF1": {
"content": "<table/>",
"text": "2 , . . . , d |D| ) be a document cluster describing the same sub-topic. P = (p 1 , p 2 , . . . , p |P | ) denotes the preferences between different versions of the generated timelines. Assuming that p 1 , p 2 , . . . , p |P | are several different pairwise labels, collected over a number of rounds, comparing several different versions of the timeline. The words, dates and keyphrases that the user wants to include and exclude are marked as M = (m 1 , m 2 , . . . , m |M | ) and N = (n 1 , n 2 , . . . , n |N | )",
"num": null,
"html": null,
"type_str": "table"
}
}
}
} |